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Let’s discuss how we can help your organization ensure that your AI and machine learning algorithms are fair and AI governance processes are robust.
The Algorithmic Bias Lab is the research and education division of BABL AI, a consultancy with deep expertise in the ethical production and deployment of artificial intelligence. With the goal of helping organizations build trust with stakeholders and the public in light of growing concerns of the misuse of these technologies, e’ve developed a holistic framework for evaluating/ auditing algorithms based:
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Fairness
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Effectiveness
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Transparency
This lab offers training courses on a variety of topics related to the ethical auditing and evaluation of algorithms. Despite our name, we recognize that bias is only part of the story, and this lab conducts research and outreach to develop and operationalize these principles in a way that gives everyone a voice in how AI should affect our lives.
Course Package Deal
AI and Algorithm Auditor Certificate Program
This packages all 5-courses into a certification program for AI and Algorithm Auditors, and also includes the bonus mini-course “Finding your place in AI ethics consulting” for free.
Note: Anyone can take the courses and get certificates of completion, but only staff or contract Algorithm Auditors working with BABL AI will obtain a certification after an exam and exit interview for each course.
Individual Courses
Finding your place in AI ethics consulting
This mini-course will outline the most common tasks performed in AI ethics consulting, and give practical advice for finding your place in this quickly growing field.
Algorithmic Risk & Impact Assessments
This course teaches you a systematic approach to algorithmic risk and ethical impact assessments, which is a necessary skill for practitioners in the space of emerging technology.
AI Governance and Risk Management
This course works covers topics in AI governance, risk management, and regulations of algorithmic systems needed for AI auditors.
Algorithms, AI & Machine Learning
This is a technical crash course in Automated Decision (Augmentation) Systems with a focus on bringing non-technical consultants, risk, and policy professionals up to speed on these emerging technologies
Bias, Accuracy, and the Statistics of AI Testing
This course works covers topics in technical testing of AI and algorithmic systems needed for auditors.
Algorithm Auditing & Assurance
This course works covers topics in AI and algorithm audit and assurance.
Research
The algorithm audit: Scoring the algorithms that score us
In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals …
The algorithm audit: Scoring the algorithms that score us
In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals …
The algorithm audit: Scoring the algorithms that score us
In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals …
Algorithmic Bias and Risk Assessments: Lessons from Practice
In this paper, we distinguish between different sorts of assessments of algorithmic systems, describe our process of assessing such systems for ethical risk, and share some key challenges and lessons for future algorithm assessments and audits. Given the distinctive …
Algorithmic Bias and Risk Assessments: Lessons from Practice
In this paper, we distinguish between different sorts of assessments of algorithmic systems, describe our process of assessing such systems for ethical risk, and share some key challenges and lessons for future algorithm assessments and audits. Given the distinctive …
Algorithmic Bias and Risk Assessments: Lessons from Practice
In this paper, we distinguish between different sorts of assessments of algorithmic systems, describe our process of assessing such systems for ethical risk, and share some key challenges and lessons for future algorithm assessments and audits. Given the distinctive …
Publication
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The Current State of AI Governance
Our interdisciplinary team at the Algorithmic Bias Lab has produced one of the very first comprehensive reports on the current state of organizational AI governance. …