AI Matters: our blog

In a recent post, AI Matters welcomed ACM SIGAI China and its members as a chapter of ACM SIGAI.  Prof. Le Dong, University of Electronic Science and Technology of China, is the Chair of SIGAI China. The AI Matters policy blog will be exploring areas of common interest in AI policy and issues for discussions in future postings.

As their first event, ACM SIGAI China held the Symposium on New Challenges and Opportunities in the Post-Turing AI Era in May, 2017, as part of the ACM Turing 50th Celebration Conference in Shanghai. Keynote presentations addressed the challenges of bringing robotic and other AI technologies into practice, including a keynote by our own Prof. Sven Koenig on timely decision making by robots and other agents in their environments.

The Symposium included workshops that particularly relate to policy issues. The Career of the Young in the Emerging Field featured rising new scientists discussing the human responsibilities and challenges that accompany the many career opportunities in AI. The Gold-Rush Again to Western China: When ACM Meets B&R workshop focused on the Belt and Road Initiative for a Trans-Eurasia, across-ocean economic strategy and the related opportunities for computer science. The IoT and Cyberspace Security workshop explored opportunities and issues in areas of vehicular sensor networks, traffic management, intelligent and green transportation, and collection of data on people and things for operating the urban infrastructure.

We look forward to interactions with our colleagues in the ACM SIGAI China as we explore policy issues along with discussing cutting-edge research in artificial intelligence.

The previous SIGAI public policy post covered the USACM-EUACM joint statement on Algorithmic Transparency and Accountability. Several interesting developments and opportunities are available for SIGAI members to discuss related topics. In particular, individuals and groups are calling for measures to provide independent oversight that might mitigate the dangers of biased, faulty, and malicious algorithms. Transparency is important for data systems and algorithms that guide life-critical systems such as healthcare, air traffic control, and nuclear control rooms. Ben Shneiderman’s Turing lecture is highly recommended on this point: https://www.youtube.com/watch?v=UWuDgY8aHmU

A robust discussion on the SIGAI Public Policy blog would be great for exploring ideas on oversight measures. Additionally, we should weigh in on some fundamental questions such as those raised by Ed Felton in his recent article “What does it mean to ask for an ‘explainable’ algorithm?” He sets up an excellent framework for the discussion, and the comments about his article raise differing points of view we should consider.

Felton says that “one of the standard critiques of using algorithms for decision-making about people, and especially for consequential decisions about access to housing, credit, education, and so on, is that the algorithms don’t provide an ‘explanation’ for their results or the results aren’t ‘interpretable.’  This is a serious issue, but discussions of it are often frustrating. The reason, I think, is that different people mean different things when they ask for an explanation of an algorithm’s results”.  Felton discusses four types of explainabilty:
1.  A claim of confidentiality (institutional/legal). Someone withholds relevant information about how a decision is made.
2.  Complexity (barrier to big picture understanding). Details about the algorithm are difficult to explain, but the impact of the results on a person can still be understood.
3.  Unreasonableness (results don’t make sense). The workings of the algorithm are clear, and are justified by statistical evidence, but the nature of how our world functions isn’t clear.
4.  Injustice (justification for designing the algorithm). Using the algorithm is unfair, unjust, or morally wrong.

In addition, SIGAI should provide input on the nature of AI systems and what it means to “explain” how decision-making AI technologies work – for example, the role of algorithms in supervised and unsupervised systems versus the choices of data and design options in creating an operational system.

Your comments are welcome. Also, please share what work you may be doing in the area of algorithmic transparency.

Algorithms in AI and data science software are having increasing impacts on individuals and society. Along with the many benefits of intelligent systems, potential harmful bias needs to be addressed. A USACM-EUACM joint statement was released on May 25, 2017, and can be found at http://www.acm.org/binaries/content/assets/publicpolicy/2017_joint_statement_algorithms.pdf. See the ACM Technology Blog for discussion of the statement. The ACM US Public Policy Council approved the principles earlier this year.

In a message to USACM members, ACM Director of Public Policy Renee Dopplick, said, “EUACM has endorsed the Statement on Algorithmic Transparency and Accountability. Furthering its impacts, we are re-releasing it as a joint statement with a related media release. The USACM-EUACM Joint Statement demonstrates and affirms shared support for these principles to help minimize the potential for harm in algorithmic decision making and thus strengthens our ability to further expand our policy and media impacts.”

The joint statement aims to present the technical challenges and opportunities to prevent and mitigate potential harmful bias. The set of principles, consistent with the ACM Code of Ethics, is included in the statement and is intended to support the benefits of algorithmic decision-making while addressing these concerns.

The Principles for Algorithmic Transparency and Accountability from the joint statement are as follows:

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.

We welcome your comments in the AI Matters blog and the ACM Technology Blog.