ACM Conference on Fairness, Accountability, and Transparency. ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT, formerly known as ACM FAT*) is a peer-reviewed academic conference series about ethics and computing systems. Sponsored by the Association for Computing Machinery, this conference focuses on issues such as algorithmic transparency, fairness in machine learning, bias, and ethics from a multi-disciplinary perspective. The conference community includes computer scientists, statisticians, social scientists, scholars of law, and others.

The conference is sponsored by Big Tech companies such as Facebook, Twitter, and Google, and large foundations such as the Rockefeller Foundation, Ford Foundation, MacArthur Foundation, and Luminate. Sponsors contribute to a general fund (no "earmarked" contributions are allowed) and have no say in the selection, substance, or structure of the conference.

FATE Overview 
The acronym FATE refers to Fairness, Accountability, Transparency, and Ethics in sociotechnical systems.  FATE is a topic of rising interest as the societal and ethical implications of complex systems such as artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) are increasing. The topic provides an interdisciplinary challenge of bridging the gap of transparency between technical and non technical academics and policy makers to ensure the safety and equity of algorithmic systems as they advance at a rapid rate.  Some solutions and techniques that have been discovered include Explainable artificial intelligence (XAI).

Recent adoptions of AI in both the public and private sector include the predictive recidivism algorithm (otherwise known as COMPAS) which was deployed in the US Court, as well as Amazon’s AI Powered recruitment tool, later proven to favor male over female applicants.  Further, AI based decision support (ADS) powered by machine learning techniques is more commonly being integrated across fields including criminal justice, education, and benefits provision.  FATE functions as a means to look further into algorithms to raise awareness and work towards a solution.  Companies such as Microsoft have created research teams specifically devoted to the topic.

Key Research Areas and Emerging Trends in FATE 
The FAccT Conference 2024 is looking for articles specifically within the following areas: Audits and Evaluation Practices, System Development and Deployment, Experiences and Interactions, Critical Studies, Law and Policy, and Philosophy.

Impact and Influence 
The research from the ACM FAccT conference has greatly influenced both public rules and how companies operate. Governments and organizations have used ideas from the conference to create guidelines and policies. For example, studies on bias in algorithms have helped change hiring methods at big tech companies, making them fairer. Additionally, laws about how artificial intelligence (AI) should be managed have been shaped by this research. The conference has also helped guide global discussions about ethical AI, contributing to important guidelines like the European Union's AI Act and the AI Principles from the OECD.

Criticisms and Controversies 
Although the ACM FAccT conference is well-regarded, it has received some criticism. Some people say that the ideas shared at the conference are often too focused on theory and may not work well for real-world issues. Others have noticed that even though the conference talks about fairness and transparency in AI, the companies that sponsor it don’t always create technology that follows these values. There is also debate about whether the conference can stay truly independent while receiving money from big tech companies.

For further reading on areas relevant to FATE see:

Algorithmic bias

Artificial intelligence art

Artificial intelligence marketing

Ethics of artificial intelligence

List of conferences
Past and future FAccT conferences include:

References

External links
 

 
 

Computer science conferences
Association for Computing Machinery conferences