With great computing power must come responsible computing
World’s largest computing society publishes inaugural issue of Journal on Responsible Computing
ASSOCIATION FOR COMPUTING MACHINERY
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AN IMPORTANT GOAL OF THE JRC IS TO ENCOURAGE SUBMISSIONS FROM AUTHORS WHO LIVE IN THE GLOBAL SOUTH, AUTHORS WHO HAIL FROM GROUPS CURRENTLY UNDERREPRESENTED IN COMPUTING AND INFORMATION, AND/OR AUTHORS WHO LIVE IN COMMUNITIES ADVERSELY AFFECTED BY INEQUITIES IN COMPUTING TECHNOLOGY.
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ACM, the Association for Computing Machinery, has announced the publication of the first issue of the ACM Journal on Responsible Computing (JRC). The new journal publishes high-quality original research at the intersection of computing, ethics, information, law, policy, responsible innovation, and social responsibility from a wide range of convergent, interdisciplinary, multidisciplinary, and transdisciplinary perspectives.
Topics addressed in JRC include values and ethics in the design and evaluation of computing and information technology; ethical and societal implications of computing and information technology; public interest technology (information technology that serves the public interest); fairness, accountability, and transparency in computing and information technology; computing, information, health, and wellbeing; approaches to addressing threats such as adversarial machine learning, misinformation, and disinformation; and examples of how computing and information can be leveraged to achieve outcomes that benefit humanity.
“Computing now impacts so many areas of our lives that a journal devoted to exploring its ethical and societal implications is essential,” explained JRC EIC Kenneth R. Fleishmann, Professor, University of Texas at Austin. “Computing professionals must be at the forefront of raising questions and conducting research about how the technologies we help develop can best serve humanity in a responsible way. Our vision for JRC is that it will be a home for outstanding research and a valued resource for everyone working in our field.”
Articles in the inaugural issues include:
- “Data Statements: From Technical Concept to Community Practice,” by Angelina McMillan-Major, Emily M. Bender, and Batya Friedman
- “Algorithmic Harms in Child Welfare: Uncertainties in Practice, Organization, and Street-level Decision-making,” by Devansh Saxena and Shion Guha
- “Between Privacy and Utility: On Differential Privacy in Theory and Practice,” by Jeremy Seeman and Daniel Susser
- “Tragedy of the Commons in Crowd Work-Based Research,” by Huichuan Xia
- “‘It’s Everybody’s Role to Speak Up...But Not Everyone Will’: Understanding AI Professionals’ Perceptions of Accountability for AI Bias Mitigation,” by Caitlin M. Lancaster, Kelsea Schulenberg, Christopher Flathmann, Nathan J. McNeese, and Guo Freeman
- “Measuring and Mitigating Group Inequalities In Resource Allocation,” by Arya Farahi, Angela Ting, and Yingchen Ma
- “Which Skin Tone Measures Are the Most Inclusive? An Investigation of Skin Tone Measures for Artificial Intelligence,” by Courtney M. Heldreth, Ellis P. Monk, Alan T. Clark, Candice Schumann, Xango Eyee, and Susanna Ricco
- “Transparency-Check: An Instrument for the Study and Design of Transparency in AI-based Personalization Systems,” by Laura Schelenz, Avi Segal, Oduma Adelio, and Kobi Gal
- “Against Predictive Optimization: On the Legitimacy of Decision-making Algorithms That Optimize Predictive Accuracy,” by Angelina Wang, Sayash Kapoor, Solon Barocas, and Arvind Narayanan
- “Data Refusal from Below: A Framework for Understanding, Evaluating, and Envisioning Refusal as Design,” by Jonathan Zong and J. Nathan Matias
An important goal of the JRC is to encourage submissions from authors who live in the Global South, authors who hail from groups currently underrepresented in computing and information, and/or authors who live in communities adversely affected by inequities in computing technology.
In addition to EiC Kenneth R. Fleischmann, the JRC editorial board consists of a 10-member advisory board, five Senior Associate Editors and 28 Associate Editors from Australia, Austria, Brazil, Canada, China, Germany, Ireland, Italy, Japan, the Republic of Korea, the Netherlands, Senegal, Sweden, the United Kingdom, and the United States.
ACM publishes more than 70 scholarly peer-reviewed journals in dozens of computing and information technology disciplines. Available online through the ACM Digital Library, ACM’s high-impact journals constitute a vast and comprehensive archive of computing innovation, covering emerging and established computing research for both practical and theoretical applications.
About ACM
ACM, the Association for Computing Machinery is the world’s largest educational and scientific computing society, uniting computing 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.
Warning from the scientific community: Beware of AI-based deception detection
UNIVERSITY OF WÜRZBURG
Oh, if only it were as easy as with Pinocchio. Here it was simple to see when he was telling a lie: after all, his nose grew a little longer each time. In reality, it is much more difficult to recognize lies and it is only understandable that scientist have already for a long time been trying to develop valid deception detection methods.
Now, much hope has been placed in artificial intelligence (AI) to achieve this goal, for example in the attempt to identity travelers with criminal intentions at the EU borders of Hungary, Greece and Lithuania.
A Valuable Tool for Basic Research
Researchers at the Universities of Marburg and Würzburg are now warning against the premature use of AI to detect lies. In their opinion, the technology is a potentially valuable tool for basic research to gain a better insight into the psychological mechanisms that underlie deception. However, they are more than skeptical about its application in real-life contexts.
Kristina Suchotzki and Matthias Gamer are responsible for the study, which has now been published in the journal Trends in Cognitive Sciences. Kristina Suchotzki is a professor at the University of Marburg; her research focuses on lies and how to detect them. Matthias Gamer is a professor at the University of Würzburg. One of his main areas of research is credibility diagnostics.
Three Central Problems for an Applied Use
Suchotzki and Gamer identify three main problems in current research on AI-based deception detection in their publication: a lack of explainability and transparency of the tested algorithms, the risk of biased results and deficits in the theoretical foundation. The reason for this is clear: "Unfortunately, current approaches have focused primarily on technical aspects at the expense of a solid methodological and theoretical foundation," they write.
In their article, they explain that many AI algorithms suffer from a "lack of explainability and transparency". It is often unclear how the algorithm arrives at its result. With some AI applications, at a certain point even the developers can no longer clearly understand how a judgment is reached. This makes it impossible to critically evaluate the decisions and discuss the reasons for incorrect classifications.
Another problem they describe is the occurrence of "biases" in the decision-making process. The original hope was that machines would be able to overcome human biases such as stereotypes or prejudices. In reality, however, this assumption often fails due to an incorrect selection of variables that humans feed into the model, as well as the small size and lack of representativeness of the data used. Not to mention the fact that the data used to create such systems is often already biased.
The third problem is of a fundamental nature: "The use of artificial intelligence in lie detection is based on the assumption that it is possible to identify a valid cue or a combination of cues that are unique for deception," explains Kristina Suchotzki. However, not even decades of research have been able to identify such unique cues. There is also no theory that can convincingly predict their existence.
High Susceptibility to Errors in Mass Screenings
However, Suchotzki and Gamer do not want to advise against working on AI-based deception detection. Ultimately, it is an empirical question as to whether this technology has the potential to deliver sufficiently valid results. However, in their opinion, several conditions must be met before it should be even considered to use in real life.
"We strongly recommend that decision-makers carefully check whether basic quality standards have been met in the development of algorithms," they say. Prerequisites include controlled laboratory experiments, large and diverse data sets without systematic bias and the validation of algorithms and their accuracy on a large and independent data set.
The aim must be to avoid unnecessary false positives - i.e. cases in which the algorithm mistakenly believes it has detected a lie. There is a big difference between the use of AI as a mass screening tool, for example at airports, and the use of AI for specific incidents, such as the interrogation of a suspect in a criminal case. "Mass screening applications often involve very unstructured and uncontrolled assessments. This drastically increases the number of false positive results," explains Matthias Gamer.
Warning to Politicians
Finally, the two researchers advise that AI-based deception detection should only be used in highly structured and controlled situations. Although there are no clear indicators of lies, it may be possible to minimize the number of alternative explanations in such situations. This increases the probability that differences in behavior or in the content of statements can be attributed to an attempt to deceive.
Kristina Suchotzki and Matthias Gamer supplement their recommendations with a warning to politicians: "History teaches us what happens if we do not adhere to strict research standards before methods for detecting deception are introduced in real life." The example of the polygraph shows very clearly how difficult it is to get rid of such methods, even if evidence of low validity and the systematic discrimination against innocent suspects accumulates later.
JOURNAL
Trends in Cognitive Sciences
METHOD OF RESEARCH
Meta-analysis
SUBJECT OF RESEARCH
People
ARTICLE TITLE
Detecting deception with artificial intelligence: promises and perils
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