AI Matters Interview: Getting to Know Maja Mataric

AI Matters Interview with Maja Mataric

Welcome!  This month we interview Maja Mataric, Vice Dean for Research and the Director of the Robotics and Autonomous Systems Center at the University of Southern California.

Maja Mataric’s Bio

Maja Mataric named as one 10 up-and-coming LA innovators to watch

Maja Matarić is professor and Chan Soon-Shiong chair in Computer Science Department, Neuroscience Program, and the Department of Pediatrics at the University of Southern California, founding director of the USC Robotics and Autonomous Systems Center (RASC), co-director of the USC Robotics Research Lab and Vice Dean for Research in the USC Viterbi School of Engineering. She received her PhD in Computer Science and Artificial Intelligence from MIT in 1994, MS in Computer Science from MIT in 1990, and BS in Computer Science from the University of Kansas in 1987.

How did you become interested in robotics and AI?

When I moved to the US in my teens, my uncle wisely advised me that “computers are the future” and that I should study computer science. But I was always interested in human behavior. So AI was the natural combination of the two, but I really wanted to see behavior in the real world, and that is what robotics is about. Now that is especially interesting as we can study the interaction between people and robots, my area of research focus.

Do you have any suggestions for people interested in doing outreach to K-12 students or the general public?

Getting involved with K-12 students in incredibly rewarding! I do a huge amount of K-12 outreach, including students, teachers, and families. I find the best way to do so is by including my PhD students and undergraduates, who are naturally more relatable to the K-12 students: I always have them say what “grade” they are in and how much more fun “school” is once they get to do research. The other key parts to outreach include letting the audience do more than observe: the audience should get involved, touch, and ask questions. And finally, the audience should get to take something home, such as concrete links to more information and accessible and affordable activities so the outreach experience is not just a one-off. Above all, I think it’s critical to convey that STEM is changing on almost a daily basis, that everyone can do it, and that whoever gets into it can shape its future and with it, the future of society.

How do you think robotics or AI researchers in academia should best connect to industry?

Recently connections to industry have become especially pressing in robotics, which has gone, during my career so far, from being a small area of specialization to being a massive and booming area of employment opportunity and huge technology leaps. This means undergraduate and graduate students need to be trained in latest and most relevant skills and methods, and all students need to be inspired and empowered to pursue skills and careers in these areas, not just those who self-select as their most obvious path; we have to proactively work on diversity and inclusion as these are clearly articulated needs by industry. There are great models of companies that have strong outreach to researchers, such as Microsoft and Google to name two, both holding annual faculty research summits and having grant opportunities for faculty to connect with their research and business units. As in all contexts, it is best to develop personal relationships with contacts at relevant companies, as they tend to lead to most meaningful collaborations.

What was your most difficult professional decision and why?

It’s hard to pick one, but here are, briefly, three that are interesting: 1) I had to actively choose whether to speak up against unfair treatment when I was still pre-tenure and in a very under-repreresented group, or to stay silent and not make waves. I spoke up and never regretted being true to myself. 2) I had to choose whether to take part of my time away from research to get involved and stay involved in academic administration. I chose to do so, but also chose to never let it take more than the official half time, and never stomp on my research. 3) I had to choose whether to leave academia for a startup or industry. These days, that is an increasingly complex choice, but as long as academia allows us to explore and experiment, it will remain the best choice.

What professional achievement are you most proud of?

The successes of my students and of my research field. Seeing my PhD students receive presidential awards while having balanced lives with families and still responding to my emails just makes me beam with pride. Pioneering a field, socially assistive robotics, that focuses on helping users with special needs, from those with autism to those with Alzheimer’s, to reach their potential. Seeing that field become established and grow from the enthusiasm of wonderful students and young researchers is an unparalleled source of professional satisfaction.

What do you wish you had known as a Ph.D. student or early researcher?

Nobody, no matter how senior or famous, knows how things are going to work out and how much another person can achieve. So when receiving advice, believe encouragement and utterly ignore discouragement. I am fortunate to be very stubborn by nature, but it was still a hard lesson and I see too many young people taking advice too seriously; it’s good to get advice but take it with a grain of salt: keep pushing for what you enjoy and believe in, even if it makes some waves and raises some eyebrows.

What would you have chosen as your career if you hadn’t gone into robotics?

I think about that when I talk to K-12 students; I try to tell them that it is fine to have a meandering path. I finally understand that what really fascinates me is people and what makes us tick. I could have studied that from various perspectives, including medicine, psychology, neuroscience, anthropology, economics, history… but since I was advised (by my uncle, see above) to go into computer science, I found a way to connect those paths. It’s almost arbitrary but it turned out to be lucky, as I love what I do.

What is a “typical” day like for you?

I have no typical day, they are all crazy in enjoyable ways. I prefer to spend my time in face-to-face interactions with people, and there are so many to collaborate with, from PhD students and undergraduate students, to research colleagues, to dean’s office colleagues, to neighbors on my floor and around my lab, to K-12 students we host. It’s all about people. And sure, there is a lot of on-line work, too, too much of it given how much less satisfying it is compared to human-human interactions, but we have to read, review, evaluate, recommend, rank, approve, certify, link, purchase, pay, etc.

What is the most interesting project you are currently involved with?

Since I got involved with socially assistive robotics, I truly love all my research projects: we are working with children with autism, with reducing pain in hospital patients, and addressing anxiety, loneliness and isolation in the elderly. I share with my students the curiosity to try new things and enjoy the opportunity to do so collaborative and often in a very interdisciplinary way, so there is never a shortage of new things to discover, learn, and overcome, and, hopefully, to do some good.

How do you balance being involved in so many different aspects of the robotics and AI communities?

With daily difficult choices: it’s an hourly struggle to focus on what is most important, set the rest aside, and then get back to enough of it but not all of it and, above all, to know what is in what category. I find that my family provides an anchoring balance that helps greatly with prioritizing.

What is your favorite CS or AI-related movie or book and why?

“Wall*E”: it’s a wonderfully human (vulnerable, caring, empathetic, idealistic) portrayal of a robot, one that has all the best of our qualities and none of the worst. After that, “Robot and Frank” and “Big Hero 6”.

Predictive Policing and Beyond

In the August 1 post, I offered a more detailed view of “algorithm” in “Algorithmic Transparency”, particularly in some machine learning software. The example was about systems involving neural networks, where algorithms in the technical sense are likely not the cause of concern, but the data used to train the system could lead to policy issues. On the other hand, “predictive” algorithms in systems are potentially a problem and need to be transparent and explained. They are susceptible to unintentional — and intentional — human bias and misuse. Today’s post gives a particular example.

Predictive policing software, popular and useful in law enforcement offices, is particularly prone to issues of bias, accuracy, and misuse. The algorithms are written to determine propensity to commit a crime and where crime might occur. Policy concerns are related to skepticism about the efficacy and fairness of such systems, and thus accountability and transparency are very important.

As stated in Slate, “The Intercept published a set of documents from a two-day event in July hosted by the U.S. Immigration and Customs Enforcement’s Homeland Security Investigations division, where tech companies were invited to learn more about the kind of software ICE is looking to procure for its new ‘Extreme Vetting Initiative.’ According to the documents, ICE is in the market for a tool that it can use to predict the potential criminality of people who come into the country.” Further information on the Slate article is available here.

The AI community should help investigate algorithmic accountability and transparency in the case of predictive policing and the subsequent application of the algorithms to new areas. We should then discuss our SIGAI position and public policy.

Algorithms and Algorithmic Transparency

Our July 15th post summarized the USACM-EUACM joint statement on Algorithmic Transparency and Accountability (ATA) and introduced the ATA FAQ project by the USACM Algorithms Working Group. Their goal is “to take the lead addressing the technical aspects of algorithms and to have content prepared for media inquiries and policymakers.” The SIGAI has been asked to contribute expertise in developing content for the FAQ. Please comment to this posting so we can collect and share insights with USACM. You can also send your ideas and suggestion directly to Cynthia Florentino, ACM Policy Analyst, at cflorentino@acm.org.

The focus of this post is the discussion of “algorithms” in the FAQ. Your feedback will be appreciated. Some of the input we received is as follows:
“Q: What is an algorithm?
A: An algorithm is a set of well-defined steps that leads from inputs (data) to outputs (results). Today, algorithms are used in decision-making in education, access to credit, employment, and in the criminal justice system.  An algorithm can be compared to a recipe that runs in the same way each time, automatically using the given input data. The input data is combined and placed through the same set of steps, and the output is dependent on the input data and the set of steps that comprise the algorithm.”
and
“Q: Can algorithms be explained? Why or why not?  What are the challenges?
A: It is not always possible to interpret machine learning and algorithmic models. This is because a model may use an enormous volume of data in the process of figuring out the ideal approach. This in turn, makes it hard to go back and trace how the algorithm arrived at a certain decision.”

This post raises an issue with the use of the term “algorithm” in the era of Big Data in which the term “machine learning” has been incorporated into the field of data analytics and data science. The AI community needs, in the case of the ATA issues, to give careful attention to definitions and concepts that enables a clear discourse on ATA policy.

A case in point, and we welcome input of SIGAI, is the central role of artificial neural networks (NN) in machine learning and deep learning. In what sense is a NN algorithmic? Toward the goal of algorithmic transparency, what needs to be explained about how a NN works? From a policy perspective, what are the challenges in addressing the transparency of a NN component of machine learning frameworks with audiences of varying technical backgrounds?

The mechanisms for training neural networks are algorithmic in the traditional sense of the word by using a series of steps repeatedly in the adjustment of parameters such as in multilayer perceptron learning. The algorithms in NN training methods operate the same way for all specific applications in which input data is mapped to output results. Only a high-level discussion and use of simplified diagrams are practical for “explaining” these NN algorithms to policymakers and end users of systems involving machine learning.

On the other hand, the design and implementation of applications involving NN-based machine learning are surely the real points of concern for issues of “algorithmic transparency”. In that regard, the “explanation” of a particular application could discuss the careful description of a problem to be solved and the NN design model chosen to solve the problem. Further, (for now) human choices are made about the number and types of input items and the numbers of nodes and layers, method for cleaning and normalizing input data, choice of an appropriate error measure and number of training cycles, appropriate procedure for independent testing, and the interpretation of results with realistic uncertainty estimates. The application development procedure is algorithmic in a general sense, but the more important point is that assumptions and biases are involved in the design and implementation of the NN. The choice of data, and its relevance and quality, are eminently important in understanding the validity of a system involving machine learning. Thus, the transparency of NN algorithms, in the technical sense, might well be explained, but the transparency and biases of the model and implementation process are the aspects with serious policy consequences.

We welcome your feedback!

USACM ATA FAQ

In the SIGAI June blog posts, we covered the USACM-EUACM joint statement on Algorithmic Transparency and Accountability (ATA). This topic is being actively discussed online and in public presentations. An interesting development is an FAQ project by the USACM Algorithms Working Group, which aims “to take the lead addressing the technical aspects of algorithms and to have content prepared for media inquiries and policymakers.” The FAQ could also help raise the profile of USACM’s work if stakeholders look to it for answers on the technical underpinnings of algorithms. The questions build on issues raised in the USACM-EUACM joint statement on ATA. The briefing materials will also support a forthcoming USACM policy event.

The FAQ is interesting in its own right, and an AI Matters blog discussion could be helpful to USACM and the ongoing evolution of the ATA issue. Please make Comment to this posting so we can collect and share your input with USACM. You can also send your ideas and suggestions directly with Cynthia Florentino, ACM Policy Analyst, at cflorentino@acm.org.

Below are the questions being discussed. The USACM Working Group will appreciate the input from SIGAI. I hope you enjoy thinking about these questions and the ideas around the issue of algorithmic transparency and accountability.

Current Questions in the DRAFT Working Document
Frequently Asked Questions
USACM Statement on Algorithmic Transparency and Accountability

Q: What is an algorithm?

Q: Can algorithms be explained? Why or why not? ? Why or why not? What are the challenges?

Q: What are the technical challenges associated with data inputs to an algorithm?

Q: What are machine learning models?

Q: What are neural networks?

Q: What are decision trees?

Q: How can we introduce checks and balances into the development and operation of software to make it impartial?

Q: When trying to introduce checks and balances, what is the impact of AI algorithms that are unable to export an explanation of their decision

Q:What lies ahead for algorithms?

Q: Who is the intended audience?

Q: Are these principles just for the US, or are they intended to applied world-wide?

Q: Are these principles for government or corporations to follow?

Q: Where did you get the idea for this project?

Q: What kind of decisions are being made by computers today?

Q: Can you give examples of biased decisions made by computer?

Q: Why is there resistance to explaining the decisions made by computer

Q: Who is responsible for biased decisions made with input from a machine learning algorithm?

Q: What are sources of bias in algorithmic decision making?

Q: What are some examples of the data sets used to train machine learning algorithms that contain bias?

Q: Human decision makers can be biased as well. Are decisions made by computers more or less biased?

Q: Can algorithms be biased even if they do not look at protected characteristics like race, gender, disability status, etc?

Q: What are some examples of proprietary algorithms being used to make decisions of public interest?

Q: Are there other sets of principles in this area?

Q: Are there other organizations is working in this area?

Q: Are there any academic courses in this area?

*********

Your suggestions will be collected and sent to the USACM Algorithms Working Group, and  you can share your input directly with Cynthia Florentino, ACM Policy Analyst

Winners of the ACM SIGAI Student Essay Contest on the Responsible Use of AI Technologies

All the submissions have been reviewed, and we are happy to announce the winners of the ACM SIGAI Student Essay Contest on the Responsible Use of AI Technologies. The winning essays argue, convincingly, why the proposed issues are pressing (that is, of current concern), why the issues concern AI technology, and what position or steps governments, industries or organizations (including ACM SIGAI) can take to address the issues or shape the discussion on them. These essays have been selected based on depth of insight, creativity, technical merit and novelty of argument.

The winners (in alphabetical order) are:

  • Jack Bandy, Automation Moderation: Finding symbiosis with anti-human technology
  • Joseph Blass. You, Me, or Us: Balancing Individuals’ and Societies’ Moral Needs and Desires in Autonomous Systems
  • Lukas Prediger, On Monitoring and Directing Progress in AI
  • Matthew Rahtz, Truth in the ‘Killer Robots’ Angle
  • Grace Su, Unemployment in the AI Age
  • Ilse Verdiesen, How do we ensure that we remain in control of our Autonomous Weapons?
  • Christian Wagner, Sexbots: The Ethical Ramifications of Social Robotics’ Dark Side
  • Dennis Wilson, The Ethics of Big Data and Psychographics

All winning essays will be published in the ACM SIGAI newsletter “AI Matters.” ACM SIGAI provides five monetary awards of USD 500 each as well as 45-minute skype sessions with the following AI researchers:

  • Murray Campbell, Senior Manager, IBM Thomas J. Watson Research Center
  • Eric Horvitz, Managing Director, Microsoft Research
  • Peter Norvig, Director of Research, Google
  • Stuart Russell, Professor, University of California at Berkeley
  • Michael Wooldridge, Head of the Department of Computer Science, University of Oxford

Special thanks are in order to our panel of expert reviewers. Each essay was read and scored by three or more of the following AI experts:

  • Sanmay Das, Washington University in St. Louis
  • Judy Goldsmith, University of Kentucky
  • H. V. Jagadish, University of Michigan
  • Albert Jiang, Trinity University
  • Sven Koenig, University of Southern California
  • Benjamin Kuipers, University of Michigan
  • Nicholas Mattei, IBM Research
  • Alexandra Olteanu, IBM Research
  • Rosemary Paradis, Lockheed Martin
  • Francesca Rossi, IBM Research

We hope to run this contest again with a new topic in the future!

— Nicholas Mattei, IBM Research

China Matters

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.

News from ACM SIGAI

We welcome ACM SIGAI China and its members to ACM SIGAI! ACM SIGAI China held its first event, the ACM SIGAI China Symposium on New Challenges and Opportunities in the Post-Turing AI Era, as part of the ACM Turing 50th Celebration Conference on May 12-14, 2017 in Shanghai. We will report details in an upcoming edition of AI Matters.

The winner of the ACM Prize in Computing is Alexei Efros from the University of California at Berkeley for his work on machine learning in computer vision and computer graphics. The award will be presented at the annual ACM Awards Banquet on June 24, 2017 in San Francisco.

We hope that you enjoyed the ACM Learning Webinar with Tom Mitchell on June 15, 2017 on “Using Machine Learning to Study Neural Representations of Language Meaning”. If you missed it, it is now available on “On Demand.”

The “50 Years of the ACM Turing Award” Celebration will be held on June 23 and 24, 2017 in San Francisco. The ACM SIGAI recipients of the ACM Turing Scholarship to attend this high-profile meeting are Tim Lee from Carnegie Mellon University and Justin Svegliato from the University of Massachusetts at Amherst.

ACM SIGAI now has a 3-month membership requirement before students who join ACM SIGAI can apply for financial benefits from ACM SIGAI, such as fellowships and travel support. Please help us with letting all students know about this new requirement to avoid any disappointments.

Algorithmic Accountability

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.

Algorithmic Transparency and Accountability

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.

USACM

As your public policy officer, I have joined the USACM.  My goals are to introduce AI matters into USACM discussions and to relay AI-related ideas and issues from USACM to SIGAI members through blog postings.
Here is some information about USACM:

Mission
The U.S. Public Policy Council of ACM (USACM) is chartered as the focal point for ACM’s interaction with U.S. government organizations, the computing community, and the U.S. public in all matters of U.S. public policy related to information technology and computing — except issues in science and math education relevant to computing and computer science, which is the responsibility of the Educational Policy Committee (EPC). The USACM Council superseded the former ACM U.S. Public Policy standing committee.

The USACM is authorized to take official policy positions.  These positions reflect the position of the USACM and not necessarily that of ACM. Policy positions of USACM are decided by a majority vote of the USACM Executive Committee.

Committees
Currently, USACM has the following seven standing committees listed below (with chairs):
USACM-Accessibility  Harry Hochheiser (Accessibility & usability)
USACM-DigiGov          Chris Bronk          (Digital governance)
USACM-IP                   Paul Hyland          (Intellectual property)
USACM-Law                Andy Grosso         (IT & Law)
USACM-Security         Alec Yasinsac        (Security)
USACM-Privacy          Brian Dean            (Privacy)
USACM-Voting           Barbara Simons    (Voting-related computing issues)

Working Groups
Internet of Things (USACM-IOT)
Algorithmic Accountability (USACM-Algorithms)
Big Data (USACM-Data)

Please find more information about USACM at http://usacm.acm.org/
and the brochure at
http://usacm.acm.org/images/documents/USACMBrochure.pdf