Guidelines  |    |  May 25, 2017

ACM Statement on Algorithmic Transparency and Accountability

Press Release:

Recognizing the ubiquity of algorithms in our daily lives, as well as their far-reaching impact, the ACM US Public Policy Council (USACM) and the ACM Europe Policy Committee (EUACM) today issued a joint statement and a list of seven principles designed to address potential harmful bias. The goals of the statement include: providing context for what algorithms are, how they make decisions, and the technical challenges and opportunities to prevent and mitigate potential harmful bias. The ACM US Public Policy Council approved the principles earlier this year. Today’s announcement demonstrates and affirms their shared support for principles to help minimize the potential for harm in algorithmic decision making.

Algorithms, the set of instructions computers employ to carry out a task, influence almost every aspect of society. The explosive growth of data collection, coupled with increasingly sophisticated algorithms, has resulted in a significant increase in automated decision making, as well as a greater reliance on algorithms in human decision making. Industry forecasters believe software programs incorporating automated decision making will only increase in the coming years as artificial intelligence becomes more mainstream. One of the major challenges of this emerging reality is to ensure that algorithms do not reinforce harmful and/or unfair biases.

A few examples of potential algorithmic bias that have been featured in government reports and news articles include: (1) Job hunting websites: Do these sites provide more listings of high-paying jobs to men than to women? (2) Credit bureaus: Does the dataset that algorithms weigh in determining credit scores contain prejudicial information? (3) Social media: What factors go into determining the news items that are served up to users?

The Statement on Algorithmic Transparency and Accountability was designed to be consistent with ACM’s Code of Ethics


Principles for Algorithmic Transparency and Accountability

  1. Awareness: Owners, designers, builders, users, and other stakeholders of analytic systems should be aware of the possible biases involved in their design, implementation, and use and the potential harm that biases can cause to individuals and society.
  2. Access and redress: Regulators should encourage the adoption of mechanisms that enable questioning and redress for individuals and groups that are adversely affected by algorithmically informed decisions.
  3. Accountability: Institutions should be held responsible for decisions made by the algorithms that they use, even if it is not feasible to explain in detail how the algorithms produce their results.
  4. Explanation: Systems and institutions that use algorithmic decision-making are encouraged to produce explanations regarding both the procedures followed by the algorithm and the specific decisions that are made. This is particularly important in public policy contexts.
  5. Data Provenance: A description of the way in which the training data was collected should be maintained by the builders of the algorithms, accompanied by an exploration of the potential biases induced by the human or algorithmic data-gathering process. Public scrutiny of the data provides maximum opportunity for corrections. However, concerns over privacy, protecting trade secrets, or revelation of analytics that might allow malicious actors to game the system can justify restricting access to qualified and authorized individuals.
  6. Auditability: Models, algorithms, data, and decisions should be recorded so that they can be audited in cases where harm is suspected.
  7. Validation and Testing: Institutions should use rigorous methods to validate their models and document those methods and results. In particular, they should routinely perform tests to assess and determine whether the model generates discriminatory harm. Institutions are encouraged to make the results of such tests public.

The ACM US Public Policy Council (USACM) serves as the focal point for ACM’s interactions with the US government in matters of US public policy related to information technology. ACM US Public Policy Council statements represent the views of the Council and do not necessarily represent the views of the Association.

The ACM Europe Policy Committee (EUACM) is a standing committee of ACM Europe. It serves as the focal point for ACM’s interactions with governmental bodies in Europe, the computing community, and the public in matters of European public policy related to computing and technology. ACM Europe Policy Committee statements represent the views of the Committee and do not necessarily represent the views of the Association.

ACM, the Association for Computing Machinery, is the world’s largest educational and scientific computing society, uniting educators, researchers and professionals to inspire dialogue, share resources and address the field’s challenges. ACM strengthens the computing profession’s collective voice through strong leadership, promotion of the highest standards, and recognition of technical excellence. ACM supports the professional growth of its members by providing opportunities for life-long learning, career development, and professional networking.