FAccT welcomes papers that advance all areas related to the sociotechnical nature of computing, inviting work from computer science, engineering, humanities, law, and the social sciences (see below for a list of relevant topics). We ask submitting authors to select one or more focus areas that best describe their papers. These focus areas are used to match submissions with Reviewers and Area Chairs who have the most relevant expertise.

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    Evaluations and evaluation practices. This includes papers that describe audits or evaluations of data, algorithms, models, systems, and applications to assess issues related to fairness, justice, safety, privacy, accountability, transparency, explainability, inclusiveness, ethics, or any risks or adverse impacts of existing computational systems on individuals, groups, and society. It also includes papers that introduce or examine evaluation metrics, measurements, or other risk identification or evaluation practices, both quantitative and qualitative. Papers that evaluate the cultural, environmental, social, or economic impact of computational systems may also be submitted to this focus area.

    Experiences and interactions. This includes work examining human experiences, needs, perceptions, and interactions with real or envisioned computational systems in order to understand their impact and to inform policy and practice. It includes work on human-computer interaction, decision support and human-in-the-loop systems, visualization, design theories and methods (e.g., co-design, speculative or critical design), participatory and deliberative methods, and studies of organizational and institutional practices.

    Law and policy. This includes work examining, proposing, or discussing both public and private regulation, governance, and legal doctrines concerning the development, deployment, and use of computational systems. It also includes work interrogating the impact and effectiveness of these frameworks.

    Normative foundations and implications. This includes work examining normative questions about data, computational systems, and related design, evaluation, and governance practices by drawing insights from philosophy of science, epistemology, moral and political philosophy, science and technology studies, and related fields.

    Power and practice. This includes work that interrogates norms, practices, and power relations around data, computational systems, and related design, evaluation, and governance approaches by drawing insights from history, anthropology and sociology, cultural studies, and related fields.

    System development and deployment. This includes work concerned with the design, development, deployment, and/or theoretical analysis of data, algorithms, models, systems, and applications, with the goal of making them, for example, more fair, just, safe, privacy preserving, accountable, transparent, explainable, inclusive, or ethical, including in specialized domains such as natural language processing, computer vision,, and information retrieval.

Topics of Interest

Listed alphabetically, topics of interest include, but are not limited to:

    AI red teaming and adversarial testing
    Algorithmic fairness and bias
    Algorithmic recourse
    (In)appropriate reliance and (over)trust in computational systems
    Assurance testing and deployment policies
    Audits of data, algorithms, models, systems, and applications
    Critical and sociotechnical foresight studies of technologies, and related policies and practices
    Cultural impacts of computational systems
    Diversity in design and development (i.e., diversity as defined along many possible dimensions, such as sociocultural, demographic, ability-based, and more)
    Environmental impacts of computational systems
    Fairness, accountability, and transparency in industry, government, or civil society
    Historical, humanistic, social scientific, and cultural perspectives on topics in this list
    Human-centered approaches to factors in fairness, accountability, and transparency
    Interdisciplinarity and cross-functional teaming in fairness, accountability, and transparency work
    Interpretability/explainability
    Justice, power, and inequality in computational systems
    Labor and economic impacts of computational systems
    Legal topics in AI (e.g., antitrust, bias and discrimination, data protection, intellectual property, mis/disinformation, and privacy)
    Licensing and liability with AI
    Moral, legal, and political philosophy of data and computational systems
    Organizational factors in fairness, accountability, and transparency
    Participatory and deliberative methods in fairness, accountability, and transparency
    Regulation and governance of computational systems
    Resistance, refusal, and contestation of computational systems
    Responsible data management and data engineering
    Risks, harms, and failures of computational systems
    Science of responsible, safe, ethical, and trustworthy AI evaluation and governance
    Social epistemology of AI
    Sociotechnical design and development of data, models, and systems
    Sociotechnical evaluations of data, models, and systems
    Sociotechnical approaches to AI safety
    Threat models and mitigations
    Transparency documentation of data, models, systems, processes, and outcomes
    Value alignment and human feedback
    Value-sensitive design of computational systems
    Values in scientific inquiry and technology design as related to FAccT issues

Topics out of scope: FAccT is an interdisciplinary conference striving to impact and shape real-world socio-technical issues. We welcome submissions with varying methodologies, epistemologies, and disciplinary orientations that all seek to address that aim. ​However, work that does not have deep engagement with the social components of computational systems or that is focused on purely hypothetical concerns is considered outside the scope of the conference.