Facial Recognition Technology: A Call for the Creation of a Framework Combining Government Regulation and a Commitment to Corporate Responsibility

At a fundamental level, the misuse of facial recognition endangers privacy, human rights, and constitutional rights. However, merely banning facial recognition will not address or solve the issues and risks inherent in the use of facial recognition. Rather than an outright ban, developing specific limitations controlling how or when facial recognition can be used in public or private spaces can better serve public interests. This paper suggests creating a framework that combines government regulation and a commitment to social responsibility by developers. Creating this multi-prong framework can help distribute the burden of regulating facial recognition technology amongst parties such as the government, the companies developing the technology, and the end-users. Finally, assessing the risk levels of different uses of facial recognition technology will further allow proper allocation and distribution of this burden amongst the parties

Towards Reflective AI:Needs, Challenges and Directions for Future Research

Harnessing benefits and preventing harms of AI cannot be solved alone through technological fixes and regulation. It depends on a complex interplay between technology, societal governance, individual behaviour, organizational and societal dynamics. Enabling people to understand AI and the consequences of its use and design is a crucial element for ensuring responsible use of AI.In this report we suggest a new framework for the development and use of AI technologies in a way that harnesses the benefits and prevents the harmful effects of AI. We name it Reflective AI. The notion of Reflective AI that we propose calls for adopting a holistic approach in the research and development of AI to investigate both what people need to learn about AI systems to develop better mental models i.e. an experiential knowledge of AI, to be able to use it safely and responsibly, as well as how this can be done and supported

Taking Politics at Face Value: How Features Expose Ideology

Previous studies using computer vision neural networks to analyze facial images have uncovered patterns in the feature extracted output that are indicative of individual dispositions. For example, Wang and Kosinski (2018) were able to predict the sexual orientation of a target from his or her facial image with surprising accuracy, while Kosinski (2021) was able to do the same in regards to political orientation. These studies suggest that computer vision neural networks can be used to classify people into categories using only their facial images.However, there is some ambiguity in regards to the degree to which these features extracted from facial images incorporate facial morphology when used to make predictions. Critics have suggested that a subject’s transient facial features, such as using makeup, having a tan, donning a beard, or wearing glasses, might be subtly indicative of group belonging (Agüera y Arcas et al., 2018). Further, previous research in this domain has found that accurate image categorization can occur without utilizing facial morphology at all, instead relying upon image brightness, color dominance, or the background of the image to make successful classifications (Leuner, 2019; Wang, 2022). This dissertation seeks to bring some clarity to this domain. Using an application programming interface (API) for the popular social networking site Twitter, a sample of nearly a quarter million images of ideological organization followers was created. These images were followers of organizations supportive of, or oppositional to, the polarizing political issues of gun control and immigration. Through a series of strong comparisons, this research tests for the influence of facial morphology in image categorization. Facial images were converted into point and mesh coordinate representations of the subjects’ faces, thus eliminating the influence of transient facial features. Images were able to be classified using facial morphology alone at rates well above chance (64% accuracy across all models utilizing only facial points, 62% using facial mesh). These results provide the strongest evidence to date that images can be categorized into social categories by their facial morphology alone

Queere KI: Zum Coming-out smarter Maschinen

Gängige Formen von Diskriminierung sowie die Reproduktion normativer Stereotype sind auch bei künstlicher Intelligenz an der Tagesordnung. Die Beitragenden erläutern Möglichkeiten der Reduktion dieser fehlerhaften Verfahrensweisen und verhandeln die ambivalente Beziehung zwischen Queerness und KI aus einer interdisziplinären Perspektive. Parallel dazu geben sie einem queer-feministischen Wissensverständnis Raum, das sich stets als partikular, vieldeutig und unvollständig versteht. Damit eröffnen sie Möglichkeiten des Umgangs mit KI, die reduktive Kategorisierungen überschreiten können

Soziale Roboter, Empathie und Emotionen : eine Untersuchung aus interdisziplinärer Perspektive

Bericht: «Soziale Roboter, Empathie und Emotionen»Robots, empathy, emotions, technology assessmen