Humboldt-Universit?t zu Berlin

Artificial?intelligence?and adaptive systems, that?learn?patterns?from?past?behavior?and historic?data, play an increasing?role in our?day-to-day?lives. We?are?surrounded?by a vast amount?of?algorithmic?decision?aids, and?more?and?more?by?algorithmic?decision?making systems, too. As a subcategory, ranked search?results?have?become?the?main?mechanism, by?which?we find content, products, places, and?people online.

Thus their?ordering?contributes not only?to?the?satisfaction?of?the?searcher, but?also?to?career?and?business?opportunities, educational?placement, and?even?social?success?of those?being?ranked.?Therefore?researchers?have?become?increasingly?concerned?with?systematic?biases and?discrimination in data-driven?ranking?models.

To?address?the?problem?of?discrimination?and?fairness in the?context?of rankings, three main?problems?have?to?be?solved: First, we?have?to?understand?the?philosophical properties?of different ranking?situations?and all important?fairness?definitions?to?be?able to?decide?which?method?would?be?the?most?appropriate?for a?given?context. Second, we have?to?make?sure?that, for?any?fairness?requirement in?a ranking?context, a formal definition?that?meets such requirements?exists. More?concretely, if a ranking?context, for example, requires?group?fairness?to?be?met, we?need an actual?definition?for?group fairness in rankings in the?first?place. Third,?the?methods?together?with?their?underlying fairness?concepts?and?properties?need?to?be?available?to a wide?range?of?audiences, from programmers, to?policy?makers?and?politicians. This thesis?contributes?the?following?to solve?the?aforementioned?problems:

1. Five?Classification?Contexts?of Fairness: We?identify?the?fairness?properties?of all important?fairness?definitions, including?the?ones?we?newly?introduce, by?relating?them?to different philosophical?understandings?of?fairness. We?introduce?five?concepts, by?which we?classify all works?that?we?present in this?thesis.
2. Fair Ranking Methods: We?present?two?group-fairness-based?frameworks, an in?processing, exposure-based?approach, and a post-processing, probabilistic?approach.
3. An Open-Source API: We?implement?our?new?fairness?frameworks?into?the?first open source?library?for?fairness in ranked?search?results, as a stand-alone?programming?library in Python and Java, as?well?as a plugin?for?the?widely?known?search-engine “Elasticsearch”.

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