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ACM Code of Ethics(also: ACM Code of Ethics and Professional Conduct)

A statement of professional ethics maintained by the Association for Computing Machinery (ACM) that sets out the moral and professional responsibilities of computing practitioners. Significantly revised in 2018, the Code explicitly addresses accessibility and inclusion, stating…

AI Accountability(also: Algorithmic Accountability, AI Governance)

The principle that developers, deployers, and operators of AI systems should be held responsible for the outcomes those systems produce, including negative effects on marginalized populations such as people with disabilities. AI accountability encompasses transparency about how…

AI Bias(also: Algorithmic Bias, Machine Learning Bias)

Systematic and unfair discrimination in AI system outputs that arises from biased training data, flawed model design, or unrepresentative assumptions embedded in the development process. In accessibility contexts, AI bias can manifest as systems that reinforce stereotypes about…

AI Fairness(also: Algorithmic Fairness, Fair AI)

The principle that AI systems should not create or reinforce unfair bias against particular groups. Standard AI fairness frameworks primarily address race and gender but are increasingly recognized as inadequate for disability, because disability is often invisible,…

AI Hallucination(also: Model Hallucination, Confabulation)

The phenomenon where an AI model generates confident, plausible-sounding responses that are factually incorrect, fabricated, or not grounded in the actual input data. In accessibility contexts, AI hallucinations pose a serious safety concern — for example, a multimodal AI…

AI Incident Database(also: AIID, AI Incident Tracker)

A publicly accessible repository that documents reported incidents where AI-driven systems have caused harm or produced negative outcomes for individuals, communities, or society. Major databases include AIAAIC (AI, Algorithmic, and Automation Incidents and Controversies), the…

AI Recourse(also: Algorithmic Recourse, AI Appeal Mechanism)

The ability of individuals negatively affected by AI-driven decisions to challenge, appeal, or seek correction of those decisions. For people with disabilities, AI recourse is particularly critical because AI systems frequently make consequential decisions about welfare…

AI contestability(also: Algorithmic contestability, AI contestation)

The principle that users should be able to challenge, question, and seek recourse against decisions or outputs made by AI systems. In accessibility contexts, contestability is particularly important for blind users who rely on AI for visual access — they need mechanisms to flag…

AI disability representation(also: AI disability simulation, Disability representation in AI)

The portrayal or simulation of disabled experiences, communication styles, or perspectives by artificial intelligence systems. AI disability representation raises significant ethical concerns: while AI can make disability awareness training more scalable and interactive, it…

AI ethics(also: Artificial intelligence ethics, Machine learning ethics)

The field concerned with ensuring that artificial intelligence systems are developed and deployed in ways that are fair, transparent, accountable, and respectful of human rights. In accessibility contexts, AI ethics addresses concerns about algorithmic bias that may disadvantage…

AI hallucination(also: Model hallucination, Confabulation)

The generation of plausible-sounding but factually incorrect or fabricated information by AI systems, particularly large language and multimodal models. In accessibility applications, AI hallucinations are especially dangerous because users who cannot independently verify visual…

AI sycophancy(also: Sycophantic AI, AI agreeableness bias)

The tendency of AI systems, particularly large language models, to provide overly affirmative, agreeable, or encouraging responses that cater to the user rather than providing accurate information. In accessibility contexts, AI sycophancy poses serious safety risks — for…

AI transparency(also: Algorithmic transparency, Model transparency)

The practice of making artificial intelligence systems understandable to users and stakeholders, including how they work, what data they use, and the confidence levels of their outputs. For assistive technology users, AI transparency enables informed decision-making about when…

Ability assumption in AI(also: Visual ability assumption, Sighted bias in AI)

The tendency of AI systems to assume users possess typical sensory, cognitive, or physical abilities, leading to inappropriate responses or instructions. In the context of visual AI assistants for blind users, ability assumptions manifest as the system asking users to "read the…

Affective Computing(also: Emotion AI, Emotional AI)

A field of AI that attempts to detect, interpret, and simulate human emotions using technologies such as facial expression analysis, voice tone detection, physiological sensors, and behavioral patterns. Affective computing raises significant accessibility and ethics concerns…

Affirmative Consent(also: Yes Means Yes)

A consent model that requires explicit, active agreement to an action - typically framed as 'yes means yes' rather than the absence of refusal. Originating in sexual-violence prevention and adopted in HCI work on consent technology, affirmative consent emphasises that silence,…

Aftercare(also: Post-Interaction Care)

Reflective or supportive activity following an intimate, intense, or sensitive interaction, in which participants check in on each other's wellbeing, discuss the experience, and address any needs that arise. The concept is drawn into HCI through consent technology research as a…

Agentive Amplifier

A framing of technical artefacts, proposed by Oosterlaken and Van Den Hoven, as things that create possibilities a person would not otherwise have — extending, not replacing, the user's own agency. Under this view the ethical significance of a technology is judged by how it…

Algorithmic Bias(also: AI Bias, Machine Learning Bias)

Systematic and unfair discrimination embedded in the outputs of algorithmic systems, arising from biased training data, flawed model design, or unrepresentative development processes. For people with disabilities, algorithmic bias manifests in multiple ways: voice assistants…

Algorithmic Discrimination(also: AI Discrimination, Automated Discrimination)

The systematic disadvantaging of specific groups through the operation of AI-driven systems, whether intentional or emergent. For people with disabilities, algorithmic discrimination occurs across many domains: employment (AI hiring tools screening out disabled applicants),…

Algorithmic Harm(also: AI Harm, Algorithmic Negative Outcome)

Any difficulty, disadvantage, or injury caused by the use of AI-driven systems, ranging from mere inconvenience to material harm. For people with disabilities, documented algorithmic harms include denial of vital resources (welfare benefits, employment, housing, education),…

Algorithmic accountability(also: AI accountability)

The principle that organizations and individuals responsible for creating and deploying algorithmic systems should be held responsible for the outcomes and impacts of those systems. In accessibility contexts, algorithmic accountability addresses who is responsible when…

Algorithmic bias(also: AI bias, Machine learning bias, Algorithmic discrimination)

Systematic and unfair errors in the outputs of automated decision-making systems that disadvantage particular groups of people. For people with disabilities, algorithmic bias arises from underrepresentation in training datasets, historical discrimination encoded in data, and…

Anthropomorphism(also: Humanization, Anthropomorphization)

The attribution of human characteristics, emotions, intentions, or behaviors to non-human entities such as technology, animals, or objects. In assistive technology and conversational AI design, anthropomorphism raises important questions about how human-like an interface should…

Applied Behavioural Analysis(also: ABA, Applied Behavior Analysis)

A therapeutic approach based on behaviorist principles that uses reinforcement and conditioning to modify behaviour, widely used with autistic children. ABA has become increasingly controversial within the autistic community and among disability scholars. Critics argue that it…

Automation Confusion

A phenomenon, well documented in human–automation literature and observed in partially automated video games, in which the user struggles to distinguish the outcomes of their own actions from those performed autonomously by a software agent. In shared-control gaming this can…

Bias Mitigation(also: Algorithmic Fairness, Debiasing)

The process of identifying and reducing systematic errors or prejudices in AI systems, datasets, and algorithms that lead to unfair outcomes for particular groups of people. In accessibility, bias mitigation is critical because AI training datasets often underrepresent people…

Biometric System(also: Biometric Technology, Biometric Identification)

A technology system that uses innate human physical or behavioral characteristics — such as facial features, fingerprints, voice patterns, gait, or iris patterns — to identify or verify a person's identity. Biometric systems pose particular risks for people with disabilities…

Bystander privacy(also: Third-party privacy, Incidental privacy)

The privacy concerns of people who are unintentionally captured or observed by technology being used by others. In the context of assistive technology, bystander privacy refers to the rights and concerns of sighted people who may be recorded, analyzed, or described by…

Capabilities approach(also: Capability approach, Human capabilities framework)

A philosophical framework developed by Amartya Sen and Martha Nussbaum that evaluates well-being and justice based on what people are actually able to do and be, rather than on the resources they possess. In disability and accessibility contexts, the capabilities approach…

Citational Justice(also: Citation Justice, Citational Politics)

The practice of consciously and equitably attributing knowledge to its sources, particularly uplifting contributions from marginalized scholars and communities whose work is often overlooked or appropriated. In accessibility research, citational justice means acknowledging…

Community Sustainability(also: Research Sustainability)

The principle that research practices should not deplete, harm, or overburden the communities from which participants are recruited. In accessibility research, community sustainability requires considering the cumulative impact of multiple studies drawing from the same…

Consent(also: Informed Consent)

Voluntary, informed, and revocable agreement by a person to a particular action or interaction involving them - whether that is sexual activity, data collection, medical treatment, research participation, or interaction with an automated system. In accessibility contexts,…

Consent Model(also: Consent Framework)

A prescriptive framework specifying how consent should be requested, given, sustained, and revoked in a particular interaction context. Examples include affirmative consent (explicit verbal agreement), embodied consent (drawing on bodily and somatic cues), and haptic consent…

Content Moderation(also: Content Filtering, Automated Content Moderation)

The process of monitoring and filtering user-generated content on digital platforms, increasingly performed by AI systems. Content moderation has documented negative effects on people with disabilities: automated systems have suppressed content from disabled creators (TikTok…

Contextual Integrity(also: CI, Contextual Privacy)

A privacy framework developed by Helen Nissenbaum that defines privacy not as secrecy but as the appropriate flow of information according to context-specific norms. According to contextual integrity, privacy is violated when information flows deviate from the norms governing a…

Critical Computing

An umbrella term for HCI and computer-science scholarship that interrogates the values, power relations, and social consequences of computing technologies rather than taking their benefits as given. Critical computing draws on disability studies, science and technology studies…

Cultural Appropriation(also: Cultural Misappropriation)

The adoption or use of elements from a minority culture by members of a dominant culture without proper understanding, acknowledgment, or respect for their original meaning and significance. In disability and accessibility contexts, this can occur when hearing researchers or…

Dark Pattern(also: Deceptive Pattern, Manipulative Design)

A user interface design deliberately crafted to trick, manipulate, or coerce users into making unintended choices that benefit the service provider rather than the user. Dark patterns include hidden costs, forced continuity, disguised ads, confirm-shaming, and misdirection. In…

Data Minimization

A privacy principle requiring that organizations collect, process, and retain only the minimum amount of personal data necessary to accomplish a specific purpose. For assistive technology users, data minimization is particularly important because these technologies often capture…

Data Representativeness(also: Dataset Representativeness, Demographic Representativeness)

The degree to which a dataset reflects the diversity of the population it is intended to serve, particularly across demographic dimensions such as age, gender, race, ethnicity, disability, and socioeconomic status. In AI and machine learning, unrepresentative training data leads…

Data Sharing(also: Open Data, Data Dissemination)

The practice of making research data available to other researchers or the public for reuse, replication, and further analysis. In accessibility research, data sharing presents unique tensions: datasets sourced from people with disabilities are essential for building inclusive…

Data Stewardship(also: Dataset Stewardship, Data Governance)

The responsible management of data throughout its lifecycle, including decisions about collection, storage, access, sharing, and disposal. In accessibility research, participatory data stewardship involves disabled data contributors in decisions about how their data is used,…

Data Transparency(also: Data Processing Transparency)

The practice of clearly communicating to users what data is collected, how it is processed, where processing occurs (on-device vs. cloud), how data is stored, and who has access to it. In accessibility contexts, blind users have expressed strong desires to understand data…

Dataset Bias(also: Training Data Bias, Data Representation Bias, Sampling Bias)

A systematic skew in the composition of training data used to build machine learning models, resulting in models that perform well for overrepresented groups but poorly for underrepresented ones. In accessibility contexts, dataset bias is a pervasive problem: activity…

Datasheets for datasets(also: Dataset documentation, Data cards)

A standardized documentation framework proposed by Gebru et al. that accompanies machine learning datasets with information about their creation, composition, intended use, and limitations. For accessibility, datasheets help surface representation gaps — such as whether people…

Dataveillance

Surveillance conducted through the systematic collection, aggregation, and analysis of personal digital data — clicks, location traces, physiological signals, text, voice, facial data — rather than through direct observation. Dataveillance is the dominant mode in modern…

Deepfake(also: Synthetic Media, AI-Generated Media)

AI-generated or AI-manipulated media (images, video, audio, or text) designed to convincingly depict events, people, or statements that never occurred. Deepfakes pose specific risks for people with disabilities: AI-generated fake images of disabled people have been used for…

Deficit-Oriented Research(also: Deficit Model, Deficit-Based Approach)

A research approach that frames its subjects primarily in terms of what they lack, cannot do, or need to have fixed, rather than recognizing their strengths, agency, and lived expertise. In disability and accessibility research, deficit-oriented approaches treat disabled bodies…

Design Saviorism

A problematic dynamic in design practice where nondisabled designers position themselves as rescuers of disabled people, seeking praise while attempting to fix something that is not broken. Design saviorism perpetuates power imbalances by centering the designer's perspective…