AI Ethics in Smart Homes: Progress, User Requirements and Challenges

Ethics, traditionally rooted in philosophy (shafer2006ethics, ), addresses fundamental questions about morality, such as determining what is right or wrong, good or bad, and the responsibilities individuals owe to one another (deigh2010introduction, ). It entails a systematic and critical examination of moral dilemmas encountered in everyday life and within professional fields like business, medicine, and technology (reynolds2006moral, ). This discipline provides a robust framework for evaluating actions not only in terms of their effectiveness or legality but also through the lens of moral responsibility, thus guiding human conduct, shaping public policies, and setting standards for professional behavior (deigh2010introduction, ). By integrating ethical principles, societies aim to promote fairness, accountability, and the well-being of individuals and communities.

Ethics can be categorized into three main areas: normative ethics, meta-ethics, and applied ethics (bonhoeffer1995ethics, ). Normative ethics focuses on determining what actions are morally right or wrong by establishing standards or norms for behavior (posner1997standards, ). It addresses practical questions, such as what one ought to do in particular situations, drawing from moral theories like utilitarianism (maximizing overall happiness), deontology (emphasizing duties and rules), and virtue ethics (highlighting moral character) (kagan2018normative, ). Meta-ethics, on the other hand, explores the nature and origins of ethical principles, delving into questions about the meaning of moral statements, the existence of moral truths, and how such truths can be known independently of individual opinions (o2001meta, ). Applied ethics deals with the application of ethical theory to specific controversial issues, such as medical ethics, business ethics, and environmental ethics, offering guidance on moral challenges faced by individuals or societies by leveraging insights from normative ethics (singer1986applied, ).

General ethics has long played a vital role in shaping human behavior and decision-making by providing a moral framework for evaluating actions (blasi1983moral, ), rather than relying solely on considerations of effectiveness, legality, or customary practices (deigh2010introduction, ). Foundational documents like the Belmont Report (paxton2020belmont, ) and the Declaration of Helsinki (goodyear2007declaration, ) have guided ethical research practices for decades. However, the advent of big data and advanced technological methods has introduced new ethical complexities, particularly in fields like behavioral research (cozby2012methods, ). These developments have made it increasingly difficult to safeguard the rights and welfare of research participants, raising the need for renewed attention to ethical considerations in modern contexts (favaretto2020first, ).

AI has impacted fields such as facial recognition (kaur2020facial, ), medical diagnostics (kononenko2001machine, ), and autonomous driving (yurtsever2020survey, ), contributing to improvements in economic, social, and safety aspects of human life. However, alongside these advancements, AI faces challenges related to explainability, data biases, and privacy concerns (arrieta2020explainable, ). AI ethics, which is an emerging direction (hagendorff2020ethics, ), seeks to address these issues by developing specific ethical frameworks to ensure AI systems operate within morally sound boundaries (miller2001boundaries, ). The goal is to identify and mitigate ethical concerns, ensuring that AI technologies adhere to established ethical standards throughout their lifecycle (siau2020artificial, ).

Favaretto et al. (favaretto2020first, ) highlight the ethical challenges posed by Big Data research, emphasizing the need for updated guidelines that reflect the complexities of modern digital research. Traditional ethical principles must evolve to address the unique challenges introduced by technologies that distance researchers from their subjects, such as those found in AI and Big Data (favaretto2020first, ).

Similarly, Hagendorff et al. (hagendorff2020ethics, ) provide a semi-systematic evaluation of 22 AI ethics guidelines, developed in response to the rapid advancements in AI systems. He emphasizes the pivotal role of ethical principles in shaping the research, development, and deployment of AI technologies, emphasizing the necessity of translating these guidelines into actionable and practical solutions to ensure their effectiveness in real-world applications.

As shown in Table  1, there are clear distinctions between general ethics, which are grounded in human reasoning, empathy, and cultural norms, and AI ethics, which require the design of autonomous systems that ensure fairness, transparency, and accountability. This distinction is particularly significant in the context of smart home technologies, where AI systems handle sensitive tasks such as healthcare monitoring (islam2020development, ), decision-making (duan2019artificial, ), and automated assistance (nirmala2022artificial, ), often for vulnerable populations like older adults.

In smart homes, ethical AI (zhou2020survey, ) development requires systems that not only align with human values but also address privacy concerns, avoid biases, and ensure accountability for unintended consequences (shneiderman2020bridging, ). These systems must respect personal autonomy, provide unbiased decision-making (e.g., in healthcare recommendations), and offer clear explanations for the actions taken by AI. Developing such frameworks is essential to safeguarding user rights while optimizing the efficiency and safety of smart home environments (powers2020ethics, ; hagendorff2020ethics, ; zhou2023ai, ).

The concept of home automation (aldrich2003smart, ), introduced in the late 20th century, initially focused on basic tasks such as controlling lighting, thermostats, and electrical appliances through centralized systems (bennett2017healthcare, ). These early systems (pirbhulal2016novel, ; jabbar2019design, ; sun2013multi, ) relied on physical wired connections and simple programming functionalities. The primary aim is to enhance convenience and comfort by automating repetitive household tasks. Although limited in scope, these early automation systems set the foundation for future innovations by demonstrating the potential of integrating technology into residential environments. Despite their high costs and the need for professional installation, they illustrated the transformative benefits of smart homes, paving the way for more scalable and user-friendly solutions in the years to come.

With advancements in internet and telecommunications, smart home systems began incorporating remote connectivity and control capabilities. Broadband internet (akerman2015skill, ) during this period played a crucial role, enabling devices to communicate more efficiently. The rise of mobile phones and early smartphones allowed homeowners to manage and monitor their homes remotely through dedicated web interfaces (serrano2013mobile, ) or mobile applications (serrano2013mobile, ), significantly increasing the practicality of smart homes. This shift from isolated systems to networked devices marked a pivotal step in home automation, bringing the concept of remote monitoring into mainstream (grubic2014servitization, ) use and making smart homes more adaptable, better acceptable and user-friendly.

The mid-2000s marked a significant expansion in wireless smart home technology, thanks to the development of interoperability standards and the introduction of wireless protocols such as ZigBee (han2014smart, ) and Z-Wave (han2010smart, ). These protocols provided standardized communication methods between devices, allowing for seamless integration of products from different manufacturers. Mesh networking (benyamina2011wireless, ) further improved network reliability by enabling devices to act as repeaters, extending coverage throughout the home. These advancements not only reduced installation complexity and cost but also increased flexibility, allowing more homeowners to embrace smart home systems (amaldi2008optimization, ). The emphasis on interoperability was key in breaking the silos that previously existed between different manufacturers’ devices, making smart home adoption more accessible and effective.

The integration of voice control technologies, including Amazon Alexa, Google Assistant, and Galaxy Watch, introduced a new dimension of convenience and interactivity to smart homes. Voice assistants (ammari2019music, ) transformed smart homes from manual, app-controlled environments to intuitive, hands-free experiences. Homeowners use simple voice commands to control lighting, temperature, entertainment systems, and even manage their daily schedules. The ability to receive personalized responses and automate tasks by speaking to a device added significant value, enhancing user engagement and making smart home technology more accessible, particularly for individuals with mobility issues or limited technical skills (pal2020adoption, ).

The late 2010s saw the integration of AI into smart homes, significantly enhancing their capabilities. AI-driven devices learn from user behavior and preferences, enabling autonomous management of household tasks without explicit programming (chang2021survey, ; zaidan2020review, ). AI systems optimize energy usage (zahraee2016application, ), manage home security by detecting unusual activity, and adapt environmental conditions such as lighting and temperature based on occupant habits. This deep level of personalization makes smart homes more responsive to individual needs, resulting in an improved living experience (aguilar2021systematic, ). AI also allows smart homes to predict user needs and automate complex routines, further simplifying daily life and improving efficiency (aguilar2021systematic, ).

The growth of IoT technologies has significantly expanded the capabilities of smart homes, allowing numerous appliances such as air conditioners, security cameras, door locks, and refrigerators to be interconnected within an integrated ecosystem (choi2021smart, ). Modern smart homes rely on a centralized controller that facilitates the seamless management of these interconnected devices. Cloud-based platforms (fylaktopoulos2016overview, ) have also become integral, providing remote access, data storage, and enhanced capabilities such as real-time monitoring. This shift toward complete ecosystems represents the transition from isolated automation to an interconnected smart home experience, with devices working together to provide increased comfort, security, and energy efficiency (li2019internet, ).

As smart homes become more interconnected, robust security and privacy measures have become crucial (yang2017survey, ). Encryption (zhou2013efficient, ), secure transmission protocols (saxena2014easysms, ), and user authentication (liu2017secure, ) are now foundational to protect sensitive user data. The implementation of privacy-by-design principles (bu2020privacy, ) ensures that data collection is minimized, and users are given control over their personal information. Smart home manufacturers are also developing multi-layered security frameworks (hong2016towards, ), incorporating elements such as regular firmware updates (zandberg2019secure, ), anomaly detection (chandola2009anomaly, ), and intrusion prevention systems (ierace2005intrusion, ) to ensure that smart home networks remain secure against potential cyber threats.

Energy efficiency and sustainability have become central priorities in the development of smart homes. Smart energy monitoring systems, integrated renewable energy sources, and energy storage solutions enable homeowners to optimize energy consumption and minimize waste (zhou2016smart, ). Devices like smart thermostats (adhikari2018algorithm, ), energy meters (sun2015comprehensive, ), and AI-driven systems (ali2024ai, ) help automate energy management, reducing consumption during peak hours and maximizing the use of renewable energy. This focus on sustainability not only reduces household costs but also contributes to broader environmental conservation efforts, making smart homes an essential component of green living (sun2015comprehensive, ).

The evolution of smart homes is accelerating with the integration of advanced technologies such as 6G (dang2020should, ), blockchain (nofer2017blockchain, ), edge computing (shi2016edge, ), and extended reality (XR) technologies (ratcliffe2021extended, ), including augmented reality (AR), virtual reality (VR), and mixed reality (MR), along with brain-computer interfaces (BCIs) (kohli2022review, ; bhattacharya2021coalition, ). BCIs offer the potential for users to interact directly with smart devices through neural commands, eliminating the need for traditional input methods. XR technologies provide immersive control interfaces, allowing users to visualize and interact with their smart home systems in novel ways (kohli2022review, ). These advancements are expected to significantly enhance not only the capabilities but also the user experience of smart homes, making them more interactive and deeply integrated into everyday human cognition and perception.

This category includes motion detectors, security cameras, and door and window sensors. AI-powered cameras now offer features like facial recognition, motion detection, and night vision, with advancements enabling them to distinguish residents from intruders and alert homeowners to potential security breaches.

Motion sensors (wu2012classification, ) detect human activity in designated areas, enabling actions such as turning on lights, triggering alarms, or adjusting temperatures. Widely used in smart homes, these sensors include wearable, ambient, and multimedia types (rashidi2012survey, ), which are essential for activity recognition and ensuring safety. Innovations like electronic textiles offer lightweight, flexible, and cost-effective alternatives to traditional sensors, accommodating various body types while tracking real-time movements (wang2020textile, ). Advanced self-driven multi-functional motion sensors (MFMS) further enhance capabilities by measuring direction, speed, and acceleration using triboelectric nanogenerators and magnetic regulation modules (narayana2018review, ; wu2018self, ), making them versatile tools for smart home applications.

Smart home security cameras (pierce2019smart, ) allow homeowners to monitor their property remotely, detecting motion, recording video, and sending alerts to devices when suspicious activity is observed. A near real-time monitoring and security system (pandya2018smart, ) for smart homes using Wireless Sensor Networks (WSNs) (akyildiz2010wireless, ) includes video cameras capable of identifying intruders, even if their faces are obscured by clothing, masks, or poor lighting conditions, with high accuracy (pandya2018smart, ).

Door and window sensors (wu2015monitoring, ) detect the opening and closing of entry points, providing data on household entries and exits. These sensors are frequently used for security purposes and can also trigger automation routines, such as turning off the HVAC system (trvcka2010overview, ) when a window is opened. A home automation system (stolojescu2021iot, ) incorporating sensors and actuators linked via a flexible API, allowing users to control devices like door and window sensors remotely through smartphone apps. Another smart home monitoring system using ESP32 microcontrollers (babiuch2020smart, ) integrates sensors and cameras with alarms and touchscreens, enhancing the security of doors and windows (babiuch2020smart, ).

These technologies (kumar2012environmental, ) detect changes in conditions such as smoke, carbon monoxide, radon, and other hazardous gases. They can alert residents to dangers even when they are away from home, and they can be integrated with other home systems to automate responses like turning on ventilation or contacting emergency services (mois2017analysis, ).

Smoke and carbon monoxide detectors (chen2007fire, ) provide early warnings of potential hazards, sending alerts to homeowners’ smartphones or triggering alarms within the house. These devices can also be connected with other systems to automatically shut off HVAC units, unlock doors, or notify emergency responders. Optimization of Smoke-Detector Installation (gu2023optimization, ) improves the placement of smoke detectors within homes to enhance detection efficiency. It emphasizes the importance of considering environmental factors like airflow, which can significantly influence the effectiveness of smoke detection and carbon monoxide absorption (gu2023optimization, ).

Glass break sensors detect (mach2019accelerometer, ) sound or vibration patterns associated with breaking glass, alerting homeowners to potential intrusions or accidents like shattered windows. The accelerometer-based system (mach2019accelerometer, ) distinguishes between actual intrusions and false alarms using an accelerometer to detect vibrations in glass panes. This technology (mach2023development, ) increases security by accurately assessing glass break signals, aiming to meet stringent safety requirements.

Energy management detection technologies (zhou2016smart, ) in smart homes include smart thermostats and systems that monitor real-time electricity usage. These devices can identify patterns in energy consumption (han2014smart, ) and automatically adjust heating, cooling, and lighting to improve efficiency and reduce costs (anvari2014optimal, ).

Smart Home Energy Management Systems (SHEMS) leverage technologies like ZigBee and Power Line Communication (PLC) to monitor and control energy consumption and renewable energy generation (zhou2016smart, ; han2014smart, ). These systems integrate ZigBee-based measurement modules and PLC gateways to optimize energy usage, manage real-time data from appliances, and support renewable sources such as solar panels and wind turbines. Advanced features, including Disjoint Multi-Path Routing (DMPR) protocols, enhance network performance by efficiently routing sensor data, reducing energy costs through intelligent scheduling (han2010smart, ). Additionally, systems like solar-powered setups with Raspberry Pi enable remote appliance control, further improving energy efficiency and lowering operating costs in everyday use (elkholy2022design, ). Together, SHEMS promote sustainable energy practices while empowering homeowners with efficient energy management tools.

Occupancy sensors (garg2000smart, ) play a key role in energy management by detecting whether rooms are occupied or vacant. These sensors can automate lighting, heating, and cooling systems to optimize energy usage based on the presence of occupants. They also provide security benefits by detecting whether a space is in use. Chaudhari et al. (chaudhari2024fundamentals, ) thoroughly examine the underlying technologies used for occupancy detection, including the sensors and algorithms that support it. These technologies integrate into smart building systems, helping to improve energy efficiency, enhance security, and support emergency response strategies. Accurate occupancy data are effectively managing smart building environments, from lighting and HVAC control to security systems.

Health monitoring technologies (huynh2018autonomous, ) in smart homes are particularly valuable for elderly care, offering devices capable of detecting falls, monitoring vital signs, and tracking movements within the home. Some advanced systems learn normal behavior patterns and can send alerts when unusual activities are detected, potentially signaling a health issue (lynch2006summary, ).

Unobtrusive health monitoring in private settings, such as homes, is facilitated by the use of advanced sensor technologies, big data, and AI, allowing for discreet health monitoring without invading privacy (wang2021unobtrusive, ). Smart homes designed for elderly healthcare (majumder2017smart, ) integrate environmental and physiological sensors to support continuous remote health monitoring. This setup enables elderly individuals to live comfortably in their homes while being continuously monitored for potential medical emergencies. In addition, wearable sensors (majumder2017wearable, ) integrated into smart home environments enable continuous tracking of vital signs, providing early detection of health issues and ensuring timely intervention.

Facchinetti et al. (facchinetti2023can, ) further explore the role of smart home technologies in managing chronic diseases among older adults. Their study highlights the potential of these technologies to help control disease exacerbations and enhance patient safety by providing continuous health monitoring, thereby supporting disease management and timely interventions.

Structural monitoring employs sensors to detect potential issues within a building’s structure, such as shifts, cracks, floods, water leaks, or foundation problems. By identifying these integrity concerns at an early stage, this technology empowers homeowners to take proactive measures, preventing minor issues from escalating into significant damage and ensuring the long-term stability and safety of the home.

Structural sensors (xu2004wireless, ) also monitor environmental factors like temperature, humidity, air quality, and noise levels, helping to maintain a comfortable indoor environment while identifying issues such as excessive humidity or poor air quality. Ullo, S. L. and Sinha, G. R. (ullo2020advances, ) explore advancements in smart structural monitoring systems, highlighting the transformative role of IoT and modern sensor technologies. Their work emphasizes the monitoring of environmental factors such as air quality, water quality, and radiation pollution, demonstrating how IoT integration enhances the precision and effectiveness of structural management, enabling more efficient and proactive maintenance strategies.

An IoT-based solution for managing indoor environments in smart buildings (floris2021iot, ) shows how IoT sensors measure parameters like temperature, humidity, air quality, and luminosity. The data collected from these sensors is integrated with machine learning to optimize building management, enhancing energy efficiency, occupant health, and overall comfort.

Smart structural sensors designed for water leak detection help homeowners prevent costly damage by identifying water where it shouldn’t be, such as around pipes, dishwashers, washing machines, or other potential leak sources. These sensors can be strategically placed in areas prone to water damage, such as basements, kitchens, and bathrooms, providing early warnings to prevent extensive damage. Water leak detectors monitor for signs of leaks or flooding, issuing alerts to homeowners that can mitigate potential water damage (li2015review, ). An integrated approach to leak detection using GIS and remote sensing has been applied in large-scale water distribution networks, offering methodologies such as infrared imaging and geographic information systems. While primarily focused on infrastructure, these techniques provide valuable insights that can be adapted for smart home water leak detection (al2023integrated, ).

These structural monitoring technologies are commonly integrated into centralized smart home systems, enabling seamless control through smartphone applications, voice assistants, or remote internet access. This integration facilitates real-time alerts, comprehensive data logging, and remote management capabilities, enhancing the safety, efficiency, and ease of managing modern homes. When combined with other smart home detection technologies, they contribute to an interconnected and intelligent ecosystem, offering homeowners improved control, convenience, and peace of mind.

Table  2 provides an overview of the most commonly used sensors in smart home detection technologies, as identified in peer-reviewed literature. The discussion of detection technologies, including different types of sensors for smart homes, highlights their transformative potential in enhancing security, energy efficiency, and convenience, but their integration into daily life also brings forth critical ethical considerations. These technologies rely on extensive data collection and advanced algorithms to automate and optimize household functions, which raises concerns about privacy, fairness, and user autonomy. Beyond their technical performance, it is essential to evaluate how these technologies align with ethical principles, ensuring they empower users while safeguarding their rights and fostering trust. This transition underscores the need for a broader exploration of the ethical frameworks that guide the design and deployment of smart home systems, shifting the focus from technological capabilities to the human-centric and societal implications.

URN’s goal-oriented approach allows stakeholders to articulate and prioritize ethical goals and values within the software system. By explicitly modeling these ethical goals, developers can ensure that ethical considerations are incorporated from the outset of the design process (amyot2010evaluating, ). For instance, Bui-Thanh (bui2007goal, ) presents a goal-oriented optimization framework that, although applied to dynamic models, can be extended to ensure software systems meet specific ethical objectives through carefully structured goals and constraints.

URN promotes the active involvement of diverse stakeholders, including end users, domain experts, and regulatory bodies, in the requirements elicitation process. This inclusive approach is essential for ethical design, as it allows for diverse ethical concerns and viewpoints to be considered. URN’s collaborative modeling capabilities foster transparency and inclusivity, ensuring ethical perspectives are embedded throughout the design process. Flechais and Sas (flechais2009stakeholder, ) underscore the importance of stakeholder involvement in creating systems that balance usability and security, highlighting the value of diverse input in ethical system design.

URN supports traceability mechanisms that link high-level ethical goals to detailed requirements and design decisions. This ensures accountability by allowing developers to demonstrate how ethical considerations are addressed throughout the development process. Additionally, URN enables impact analysis, allowing developers to evaluate the ethical implications of changes to the system. Ramesh (ramesh2001toward, ) explores traceability in software development, showing how URN’s traceability features ensure that ethical goals remain visible and actionable throughout the project lifecycle.

UCM provides visual representations of system behaviors, interactions, and scenarios, making it easier to identify and analyze ethical dilemmas and decision points. By modeling different ethical scenarios and exploring alternative actions, developers can make informed decisions aligned with ethical principles. Kazman et al. (kazman1996scenario, ) demonstrate how scenario-based analysis simplifies the evaluation of systems, making it an effective tool for analyzing ethical consequences in software design.

RDD approach helps developers allocate ethical responsibilities to specific components or actors within the system. This method promotes ethical accountability by ensuring that designated entities are responsible for key ethical concerns, such as privacy protection or mitigating biases in AI systems. Amyot (amyot2010evaluating, ) notes that RDD aligns well with modern software engineering practices, emphasizing the need for systems to be adaptable and ethically sound, which is essential in maintaining accountability and addressing ethical risks.

URN provides a comprehensive, structured approach to integrating ethical considerations into software design. By leveraging its goal-oriented modeling, stakeholder involvement, traceability, scenario-based analysis, and responsibility allocation, developers can design systems that prioritize ethical values, respect user autonomy, and contribute to positive social outcomes.

Smart home technology integrates various devices such as smart bulbs, thermostats, security cameras, and voice assistants, all of which must work together seamlessly in different scenarios. URN allows designers to create detailed models that show how these devices interact with users and each other across multiple use cases. This helps ensure that the system performs reliably under real-world conditions, addressing the complexity of interactions between devices and user behaviors.

This enables designers to define and prioritize goals and requirements for smart home systems. For instance, users may desire automatic temperature adjustments, energy optimization, and robust security measures. GRL helps break down these high-level goals into actionable technical requirements, ensuring that user preferences are clearly articulated and systematically addressed during development.

This component of URN provides a visual representation of system behaviors and interactions, allowing designers to uncover potential conflicts, dependencies, and redundancies. By modeling various user scenarios—such as ”arriving home in the evening” or ”leaving the house unattended”—UCM ensures that the smart home system operates according to user needs across different contexts, enhancing its adaptability and usability.

Smart home systems must consider the diverse needs of all users, including the elderly and disabled. URN facilitates the inclusion of special requirements during the design phase, ensuring systems are accessible and customizable to fit unique household needs. With URN, designers can offer flexible customization options, allowing users to personalize system settings based on individual preferences, improving user experience and satisfaction.

Privacy is a critical concern in smart home environments. URN enables designers to model and analyze privacy requirements, ensuring that sensitive user data is protected throughout the system. For instance, URN can be used to model the camera’s field of view or data storage strategies, ensuring that private spaces are not unnecessarily monitored and that data is handled securely.

URN’s modeling capabilities allow designers to identify and mitigate privacy risks posed by detection technologies during the design phase (amyot2011user, ). For example, URN can be used to assess the field of view of cameras or the strategies for handling recorded data, ensuring private areas are not inadvertently monitored. By integrating encryption and access control measures into the design, URN helps safeguard user data against unauthorized access.

When designing detection technologies, URN also helps identify potential biases. For example, sound recognition devices may struggle to accurately interpret certain accents or languages. By modeling these issues, URN allows designers to anticipate and resolve bias-related challenges. URN further provides a structured approach to evaluating the performance of detection technologies across diverse user groups, ensuring fairness and accessibility for all users.

Using the RDD method, designers can clearly assign responsibilities for maintaining privacy and security within the system. This clarity ensures that if issues arise, the responsible parties can be quickly identified and held accountable, allowing for prompt corrective actions. URN fosters transparency and accountability, defining clear roles for all stakeholders—users, developers, and regulatory bodies—in the design and implementation of the system.

URN provides a comprehensive and structured framework for developing smart home technologies that not only satisfy technical specifications but also prioritize critical ethical considerations such as privacy, security, fairness, and inclusivity. By harnessing the capabilities of URN, developers can create smarter, safer, and ethically responsible systems that are thoughtfully aligned with user needs and societal values, ensuring a balance between innovation and ethical accountability.

The integration of detection technologies into smart homes raises significant concerns about privacy and data protection. Devices such as smart cameras, voice assistants, and motion detectors collect extensive amounts of sensitive data, including visual, auditory, and behavioral patterns of residents (muslukhov2012understanding, ). To mitigate these risks, robust data protection mechanisms must be employed, including encryption for both data transmission and storage. Furthermore, clear policies are needed to regulate data access and sharing, ensuring that data is used exclusively for its intended purposes. Users should maintain control over their personal data through transparent consent mechanisms and accessible privacy settings.

The Privacy by Design (perera2016privacy, ) framework is instrumental in embedding privacy considerations into the technology’s development from the outset, rather than as an afterthought. This approach advocates for data minimization, collecting only the data necessary for specific purposes and ensuring encryption throughout its lifecycle. Additionally, default privacy settings should be set at the highest level, allowing users to modify them as needed. By integrating these principles, Privacy by Design not only safeguards user data but also fosters trust by demonstrating a commitment to privacy across the product lifecycle.

Detection technologies, including facial recognition and behavior-tracking algorithms, are prone to perpetuating biases if not carefully managed. These biases can arise from imbalanced training datasets or flawed algorithmic designs, leading to discriminatory outcomes based on race, gender, or socioeconomic status (mehrabi2021survey, ). To address these issues, developers must ensure the use of diverse datasets for training and regularly test algorithms to detect and mitigate biases. Mechanisms should be established to promptly correct any unfair outcomes that may occur.

Ensuring fairness in algorithmic decision-making (starke2022fairness, ) is vital to prevent the amplification of existing social biases. In smart homes, this includes algorithms governing everything from energy management to security systems. Developers should adopt techniques to identify and mitigate biases during both the data preparation and algorithm development stages. This can be achieved through regular fairness audits, the use of unbiased datasets, and the implementation of models capable of dynamically adjusting to prevent discriminatory outcomes.

Transparency and explainability (balasubramaniam2022transparency, ) are essential for fostering user trust, particularly in autonomous systems. Users need to be informed about what data is being collected, how it is processed, and how decisions are made by smart home technologies (balasubramaniam2022transparency, ). For instance, if an anomaly detection system triggers an alert, the user should be provided with a clear explanation of the alert and the reasoning behind the system’s decision. This transparency enables users to make informed choices about privacy and security settings.

In smart home technologies, transparency involves clearly communicating how, why, and what data is collected, as well as how it is processed. Explainability further ensures that users can understand the rationale behind the decisions made by AI systems. This might involve developing user interfaces that provide concise explanations of system actions. For example, if a smart thermostat automatically adjusts the temperature, the system should offer a clear explanation via a user interface or app. These measures are essential for building trust and enabling users to manage their privacy and security settings effectively.

Accountability in smart home detection technologies requires clearly defining who is responsible for the actions and outcomes of these systems (smith2021clinical, ). Developers must be held accountable for the performance and ethical compliance of their products. This includes being responsible for any data breaches, misuse of technology, or unintended consequences that arise from their systems. Establishing regulatory frameworks is crucial to ensure that companies adhere to ethical standards.

Robust accountability mechanisms are essential to ensure that designers of smart home technologies are held responsible for the ethical implications of their products. This requires the establishment of clear standards and regulations to address issues such as privacy breaches, biased decision-making, and misuse of data. Furthermore, user feedback channels should be implemented, enabling individuals to report concerns directly to regulatory bodies or relevant authorities. Such mechanisms not only safeguard user rights but also foster transparency and drive the continuous improvement of smart home technologies, promoting ethical innovation and trust.

Preserving human autonomy is crucial as smart home technologies become more automated. These technologies should enhance, rather than diminish, users’ decision-making capabilities (barnes2017humans, ). Systems should be designed to assist users, allowing them to intervene and retain final control over decisions. For instance, while automated systems may suggest or pre-configure settings based on user behavior, the ultimate decision should always rest with the user, with simple options available to override the system’s decisions.

Maintaining human autonomy necessitates designing smart home technologies that empower users with full control over their environment. This involves ensuring that significant decisions are never made without the user’s explicit consent and that users can effortlessly override automated actions when needed. Achieving this requires transparent disclosure of device functionalities and data processing practices to secure informed consent. For instance, while systems can suggest or pre-configure settings based on user behavior, the ultimate decision must rest with the user, supported by intuitive and readily accessible options to modify or override these automated configurations.

These ethical guidelines aim to ensure that smart home technologies better protect user privacy, promote fairness, and build trust. By addressing these considerations, smart home systems can integrate more ethically into daily life, fostering responsible and user-centric innovation.

Fairness-aware algorithms are designed to detect and mitigate biases in both data and model predictions, ensuring equitable outcomes across different demographic groups. Techniques such as re-sampling, re-weighting, and applying fairness constraints are commonly used to promote fairness in algorithmic decision-making. In the rapidly evolving landscape of smart home technologies, human-in-the-loop IoT systems are increasingly able to autonomously adapt to human and environmental conditions. However, the inherent variability in human behavior presents a significant challenge to maintaining algorithmic fairness. To address this, FaiR-IoT (elmalaki2021fair, ), a reinforcement learning-based framework, enhances personalization, performance, and fairness. This framework has demonstrated success in applications like smart homes, leading to substantial improvements in both fairness and overall system performance (pirzada_ethics_2022, ; zhu_ethical_2022, ).

The concept of fairness in Demand Side Management (DSM) systems (baharlouei2014efficiency, ) introduces optimal billing mechanisms that equitably distribute energy costs based on individual user contributions to minimize overall system costs in smart homes. This approach ensures fairness by adhering to resource allocation principles, while also protecting user privacy through the implementation of the secure sum algorithm (birchley_smart_2017, ). This mechanism not only enhances cost efficiency but also upholds fairness and privacy within energy management systems.

To minimize inherent biases, training data is curated to ensure it includes diverse and representative samples. Regular dataset audits are conducted to identify and correct imbalances, ensuring fairness in model predictions. In addressing the security challenges of IoT devices, Majumdar et al. (majumdar2021security, ) presents a methodology that extracts actionable security rules from established standards and best practices, applying them to low-level device data to conduct thorough audits. This methodology is evaluated for its efficiency and scalability within smart home environments, highlighting the critical role that diverse datasets and rigorous auditing play in ensuring robust IoT security.

New models and frameworks are being developed to enhance the interpretability of AI decisions, making them more understandable to humans. Techniques such as Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Anchors provide valuable insights into how models reach their conclusions (zhou2021evaluating, ). In the domain of remote caregiver monitoring, a study evaluated both machine learning experts and non-experts, finding that SHAP-generated explanations were 92% accurate, with 83% of users preferring detailed explanations over simple activity labels (das2023explainable, ). While certain explainable AI (XAI) methods can enhance user trust in activity recognition models, others may undermine it, highlighting the ethical importance of selecting the appropriate XAI approach (zhou2021evaluating, ). Furthermore, in sensor-based recognition of Activities of Daily Living (ADL) within smart home environments—crucial for healthcare and ambient assisted living—XAI plays a key role in fostering trust and transparency. The DeXAR framework (arrotta2022dexar, ) converts sensor data into semantic images, allowing the application of XAI methods based on Convolutional Neural Networks (CNN) (wu2017introduction, ), which then generate natural language explanations. Among these methods, white-box XAI, using prototypes, proved to be the most effective in ensuring ethical transparency and trustworthiness in ADL recognition systems, reinforcing the importance of ethical design in smart home technologies (arrotta2022dexar, ).

This approach enables models to be trained across multiple devices or servers that hold local data samples, without requiring the exchange of raw data, thus enhancing data privacy. FedHome (wu2020fedhome, ), an ethical cloud-edge federated learning framework that preserves user privacy by keeping data local while building a shared global model. It utilizes a generative convolutional autoencoder to deliver precise and personalized health monitoring, along with efficient cloud-edge communication to manage imbalanced and non-identically distributed data. Experiments using realistic human activity recognition datasets demonstrate that FedHome outperforms existing methods, underscoring the ethical importance of scalable, privacy-preserving in-home health monitoring systems (wu2020fedhome, ). Additionally, LoFTI (yu2020learning, ) introduces a federated multi-task learning framework designed to ethically and automatically learn contextual access control policies from user behavior in smart homes. It addresses challenges such as data insufficiency, diversity, and privacy through a privacy-preserving federated approach. By capturing contextual access patterns using six identified feature types and employing a ML model with temporal structure and custom data augmentation, LoFTI significantly reduces false negatives (by 24.2%) and false positives (by 49.5%) compared to traditional methods. This highlights the critical role of ethical AI in developing secure, privacy-conscious smart home systems (yu2020learning, ).

Models are increasingly designed to be robust against adversarial attacks that could manipulate inputs to produce incorrect or harmful outputs, ensuring reliability and security in AI systems. ADDetector (li2021federated, ), a privacy-preserving Alzheimer’s detection system that employs advanced security measures to safeguard sensitive data. ADDetector collects audio data and extracts topic-based linguistic features to improve diagnostic accuracy. It operates using a three-layer architecture—user, client, and cloud—to ensure data privacy. The system integrates federated learning (FL) and differential privacy (DP) to maintain data confidentiality and integrity, and it utilizes an asynchronous, privacy-preserving aggregation framework to secure model updates. Tested on 1,010 trials from 99 users, ADDetector achieved an 81.9% accuracy rate with just a 0.7-second time overhead. This highlights the ethical importance of privacy-preserving AI, particularly in sensitive healthcare applications where user data security and model accuracy are paramount.

Implementing secure coding practices and conducting regular security assessments are essential for protecting AI systems from vulnerabilities and threats. The Anonymous Secure Framework (ASF) (kumar2017anonymous, ) addresses ethical concerns related to security and privacy by utilizing lightweight operations that enhance security with minimal user intervention. ASF provides efficient mechanisms for authentication, key agreement, device anonymity, and unlinkability, using a one-time session key progression to reduce the risk of compromised keys. This framework offers lower computational complexity while improving security compared to existing schemes, underscoring the ethical imperative for robust, privacy-preserving mechanisms in smart homes (kumar2017anonymous, ).

To tackle security challenges in smart home systems, another framework combines secure AI and blockchain technology (shah2023ai, ). This framework utilizes the Isolation Forest (IF) (staerman2019functional, ) algorithm to filter anomalies from a standard smart home dataset, followed by the application of classification algorithms such as k-nearest Neighbors (KNN) (guo2003knn, ), Support Vector Machines (SVM) (joachims1998making, ), Linear Discriminant Analysis (LDA) (lu2003face, ), and Quadratic Discriminant Analysis (QDA) (scikit-learn_lda_qda, ) to distinguish attack data from non-attack data. To safeguard the integrity of the data, the Interplanetary File System (IPFS) securely stores non-attack data, with its hash recorded on the blockchain’s immutable ledger to prevent data manipulation. This framework demonstrated high performance, with KNN achieving 99.53% accuracy during training and IF reaching 99.27% accuracy in anomaly detection, alongside strong validation and scalability metrics. These results showcase its effectiveness in enhancing IoT security and ensuring the ethical implementation of AI in smart homes (shah2023ai, ).

Integrating AI ethics into the development of algorithms and models marks a significant advancement in creating smart home technologies that are fairer, more transparent, and secure. As we move forward, a continued emphasis on strengthening cybersecurity and network infrastructure will be crucial in safeguarding these innovations, ensuring that smart homes remain both safe and resilient against emerging cyber threats and vulnerabilities.

With the introduction of low-power sensors, radios, and embedded processors, smart homes are equipped with a vast network of sensors that collaboratively process data to monitor household activities (ding2011sensor, ). Sensors like accelerometer-based devices and electronic adherence monitoring systems have transformed health data collection by providing unobtrusive, real-time monitoring of behaviors related to psychological health, such as sleep, physical activity, and medication adherence (psihogios2024ethical, ). Given the integration of healthcare within smart homes, ensuring the security and privacy of sensitive data is essential.

Time-critical IoT (taher2021secure, ) applications in healthcare, for instance, demand secure, real-time access to private information, making authentication in WSNs a priority. A secure, lightweight three-factor authentication (3FA) protocol (taher2021secure, ) for IoT WSNs, combining hash and XOR (taher2021secure, ) operations, and integrating biometrics and user password authentication for enhanced security. The protocol’s efficiency, security, and functionality were validated through simulations, demonstrating its superiority over existing protocols in terms of communication and computation overhead (taher2021secure, ).

WSNs (shin2019lightweight, ) form the backbone of smart home systems, monitoring environments and controlling devices. However, these networks face significant security threats. An improved lightweight three-factor authentication and key agreement scheme has been proposed (shin2019lightweight, ) to address weaknesses in earlier schemes, providing detailed security and performance analysis compared to related methods. Additionally, novel privacy-preserving methods (ali2022novel, ) for smart grid-based Home Area Networks use homomorphic encryption and hash functions to aggregate data and detect attacks, fostering trust through transparent communication.

Smart home IoT devices, often embedded with microcontroller unit (MCU) based systems, rely on specialized operating systems to manage hardware, software, and communication (mocrii2018iot, ). AI ethics for IoT operating systems revolve around privacy, security, transparency, accountability, and fairness in the management of smart devices.

Table  5 outlines key IoT operating systems, highlighting their primary use cases, programming languages, and ethical considerations. Each system—whether Contiki, RIOT, or Android Things—plays a role in ensuring smart homes meet technical and ethical standards, emphasizing privacy, security, transparency, and accountability in IoT management.

Mobile apps in smart homes, particularly those integrating AI, present ethical challenges surrounding privacy and security. These apps control IoT devices in a Home Area Network, typically relying on Wi-Fi routers for device authentication and protection against unauthorized apps (demetriou2017hanguard, ).

HanGuard (demetriou2017hanguard, ) is a technique that controls communication between IoT devices and their associated apps, employing a software-defined networking (SDN)-like method for fine-grained protection. It requires no changes to IoT devices, apps, or phone operating systems, but ensures that unauthorized access attempts are blocked through router-based verifications. This approach exemplifies how smart home applications can uphold privacy and security without compromising user convenience or system performance.

The integration of AI in smart home mobile apps offers significant benefits, but it also raises critical ethical concerns. Addressing these requires stringent privacy and security measures to ensure that mobile technologies improve daily life without compromising ethical standards.

Incorporating AI ethics into smart home detection technologies is crucial for ensuring fairness, transparency, security, and privacy. Addressing these ethical concerns through advanced algorithms—such as those ensuring algorithmic fairness, diverse datasets, explainable AI, federated learning, adversarial robustness, and secure AI—greatly enhances the reliability and performance of smart home systems. As smart homes evolve, prioritizing cybersecurity, network integrity, and the ethical design of hardware and software will be essential for safeguarding users against potential threats. This comprehensive approach ensures that AI-driven smart home technologies are deployed responsibly, enhancing quality of life while protecting user rights and data integrity.