Leading the way towards more ethical AI,
to ensure AI applications are fair, transparent and accountable
We are working to make Sony a leader in AI ethics. We will focus on conducting cutting-edge research on challenging real-world problems facing Sony Group’s businesses, which span imaging and sensing solutions, games, music, movies and finance. In a field that is currently dominated by U.S. tech companies and European regulatory standards, we believe Sony AI can offer a distinctively diverse and global perspective.
Ethical Data Collection
Ethical AI starts with ethical data collection. This project area focuses on the technical, legal and governance aspects of collecting data ethically, particularly when sensitive or personal data is involved.
Bias Detection and Measurement
Developing fairer AI systems requires reliable methods for measuring algorithmic bias. This project area focuses on developing bias checking tools and fairness benchmarks. To this end, this project area also examines the challenges of adequately measuring diversity in human-centric datasets.
Algorithmic Bias Mitigation Techniques
There is no such thing as a completely unbiased model. As a result, bias mitigation techniques are critical for ethical AI development. This project area seeks to develop better techniques for addressing algorithmic bias, including exploring concepts of fairness routed in causality.
Feature and Label Embedding Spaces Matter in Addressing Image Classifier Bias
This paper strives to address image classifier bias, with a focus on both feature and label embedding spaces. Previous works have shown that spurious correlations from protected attributes, such as age, gender, or skin tone, can cause adverse decisions. To balance potential …
On the Validity of Arrest as a Proxy for Offense: Race and the Likelihood of Arrest for Violent Crimes
The risk of re-offense is considered in decision-making at many stages of the criminal justice system, from pre-trial, to sentencing, to parole. To aid decision makers in their assessments, institutions increasingly rely on algorithmic risk assessment instruments (RAIs). The…
Reconciling Legal and Technical Approaches to Algorithmic Bias
In recent years, there has been a proliferation of papers in the algorithmic fairness literature proposing various technical definitions of algorithmic bias and methods to mitigate bias. Whether these algorithmic bias mitigation methods would be permissible from a legal pers…