Algorithmic systems, driven by large amounts of data, are increasingly being used in all aspects of society to assist people in forming opinions and taking decisions. For instance, search engines and recommender systems amongst others are used to help us in making all sort of decisions from selecting restaurants and books, to choosing friends and careers. Other systems are used in school admissions, housing, pricing of goods and services, job applicant selection, and so forth. Such algorithmic systems over enormous opportunities, but they also raise concerns regarding how fair they are. How much trust can we put in these systems?
We will analyze fairness risks through well-known use cases. Then, we will present some representative models and methods for fairness in search engines and recommender systems. We will conclude our journey to algorithmic fairness by discussing challenges and critical research paths for future work. [Go to the full record in the library's catalogue]
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