Animesh Mukherjee is an Associate Professor and the A. K. Singh Chair at the Department of Computer Science and Engineering, IIT Kharagpur, West Bengal, India. His main research interests center around investigating hate and abusive content on social media platforms, fairness, bias in information retrieval systems, media bias, and quality monitoring of Wikipedia articles. He publishes and is on the committee of most of the top AI conferences, including The Web Conference, NeurIPS, AAAI, IJCAI, ACL, EMNLP, NAACL, Coling, CSCW, ICWSM, etc.

Juhi Kulshrestha is an Assistant Professor at the Department of Computer Science at Aalto University, Finland. She obtained a doctoral degree from the Max Planck Institute for Software Systems, Germany. She also pursued research at the University of Konstanz and Leibniz Institutes for Social Sciences and Media Research. Her research, at the intersection of computer science and social science, broadly focuses on leveraging digital behavioral data to quantify and characterize how people consume news and information on the web and its effect on society. She regularly publishes in and serves on the committees of top-tier venues such as TheWebConf, AAAI ICWSM, ACM CSCW, and ACM FAccT. She is a recipient of several internationally competitive research grants such as Metäs Foundational Integrity Research Grant, Social Science One¿s Social Media and Democracy Research Grant, Google European Doctoral Fellowship for Social Computing, and Google Anita Borg Scholarship.

Abhijnan Chakraborty is an Assistant Professor at the Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Delhi. He is also associated with the School of Artificial Intelligence and the School of Information Technology at IIT Delhi. His research interests fall under the broad theme of Computing and Society, covering the research areas of Social Computing, Information Retrieval and Fairness in Machine Learning. In the past, he has worked at the Max Planck Institute for Software Systems, Germany and Microsoft Research India. During PhD, he was awarded the Google India PhD Fellowship and the Prime Minister's Fellowship for Doctoral Research. He regularly publishes in top-tier computer science conferences including WWW, KDD, AAAI, AAMAS, CSCW and ICWSM. He has won INAE Young Engineer 2022 award, the best paper award at ASONAM'16 and best poster award at ECIR'19. He is one of the recipients of an internationally competitive research grant from the

Data Transparency Lab to advance his research on fairness and transparency in algorithmic systems.

Srijan Kumar is an Assistant Professor at the School of Computational Science and Engineering, College of Computing at the Georgia Institute of Technology. He completed his postdoctoral training at Stanford University, received a Ph.D. and M.S. in Computer Sciencefrom the University of Maryland, College Park, and B.Tech. from the Indian Institute of Technology, Kharagpur. He develops Data Mining methods to detect and mitigate the pressing threats posed by malicious actors (e.g., evaders, sockpuppets, etc.) and harmful content (e.g., misinformation, hate speech etc.) to web users and platforms. He has been selected as a Kavli Fellow by the National Academy of Sciences, named as Forbes 30 under 30 honoree in Science, ACM SIGKDD Doctoral Dissertation Award runner-up 2018, and best paper honorable mention award from the ACM Web Conference. His research has been covered in the popular press, including CNN, The Wall Street Journal, Wired, and New York Magazine.

Making Socially Sustainable and Ethical AI Systems: Integrating Impact Assessment in the Co-Design Approach.- Discrimination in Advertising and Personalization.- Biases and Ethical Considerations in ML Pipelines In The Computational Social Sciences.- Operationalizing Fairness.- Achieving Group and Individual Fairness in Clustering Algorithms.- Fair Allocation of Structured Set Systems.- Algorithmic Fairness for Decisions Across Time.- Algorithmic Fairness in Multi-stakeholder Platforms.- Fairness Testing, Debugging and Repairing.- Interpretability of Machine Learning Models.