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WeAreDevelopers LIVE • Oct 12, 2020

Algorithmic Bias- Preventing Unfairness in your Algorithms

Prathyusha Charagondla

Are your algorithms accidentally amplifying historical prejudices? Learn how to abandon technochauvinism and implement fairness by design to prevent unintended bias in your machine learning models.

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#1 about 4 min

The impact of biased training data in recruitment platforms

An automated hiring tool demonstrates how imbalanced dataset proportions reproduce existing workplace demographics and unintended gender biases.

#2 about 7 min

Defining algorithmic bias in commercial computer vision systems

Systematic algorithmic errors produce unfair outcomes and expose poor accuracy across intersectional demographics in standard classification models.

#3 about 5 min

Real-world failures in predictive policing and exam grading algorithms

Unaudited logic utilized for suspect identification and automated grade calculation causes widespread harm by enforcing unearned demographic advantages.

#4 about 1 min

Preparing for algorithmic regulation and future compliance standards

Governments will likely introduce broad compliance guidelines similar to GDPR to strictly mandate ethical machine learning management.

#5 about 2 min

Overcoming technochauvinism and evaluating algorithmic training dataset integrity

Acknowledging that technical systems are inherently fallible is the crucial prerequisite before performing exploratory data analysis to isolate biased metrics.

#6 about 3 min

Embedding fairness into algorithmic design and continuous evaluation pipelines

Implementing proactive fairness by design requires adjusting pipeline variables during staging and auditing production outputs against expected baseline parameters.

#7 about 7 min

Adopting structured ethical frameworks for trustworthy artificial intelligence

Adhering to structured institutional guidelines guarantees consistent human oversight, system robustness, and equitable impact distribution across vulnerable user populations.

#8 about 2 min

Mitigating groupthink through diverse and inclusive software engineering teams

Diverse organizations consistently leverage varied professional viewpoints to identify hidden execution assumptions and proactively prevent unintended structural consequences.

#9 about 4 min

Embedding human values and accountability into complex predictive models

Software professionals act as explicitly responsible data stewards by actively auditing intelligent systems to prevent automated systemic discrimination at a meaningful scale.

Matching moments

2:18 min

Addressing implicit human biases embedded within algorithmic datasets

Cassie Kozyrkov · WWC 2022

4:41 min

Identifying systemic biases in algorithms and artificial intelligence tools

Emily Wright Emily Wright · Europe 2026 Virtual

3:08 min

Implementing responsible artificial intelligence frameworks to mitigate model bias

Alexander Wallner Alexander Wallner +3 · WWC 2024

1:49 min

Mitigating algorithmic bias during AI model development

Björn Bringmann Björn Bringmann +3 · WWC 2024

2:26 min

Addressing algorithmic bias and discrimination in decision systems

Christoph Bräunlein Christoph Bräunlein +3 · WWC 2025

2:42 min

Mitigating data bias and explaining AI decisions

Werner Vogels Werner Vogels +1 · WWC Europe 2026

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