The document discusses various topics related to responsible and ethical AI, including realizing responsible AI in practice, bias in healthcare algorithms, revealing underlying AI clinical decision systems, AI misdiagnoses, detecting mental health issues from social media, and predicting teenage pregnancy. It also discusses the impacts of a lack of responsible AI, such as lost revenue and customers. Additionally, it presents data on organizations' responsible AI programs and priorities, and discusses integrating responsible AI practices like bias monitoring, explainability, and privacy across the data science lifecycle.
7. AGILE MUMBAI 2022
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AI can detect mental health
issues on social media
posts
Bias in healthcare decision-
making algorithms
Medical practitioners AI clinical
decision support systems
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A lack of responsible AI and it’s impact
62% reported lost revenue
61% lost customers
43% lost employees
35%
incurred legal fees
due to lawsuits or
legal action
Source: DataRobot Survey, 2021
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52% of organizations
have RAI programs
Leaders agree that responsible AI should be a top management concern, few
have prioritized such initiatives.
84% believe RAI
should be a top
management priority
20% have a fully
implemented responsible
AI program
Source: 2022 Responsible AI Global Study, MIT
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Data science lifecycle with RAI
Input data
EDA, Pre-processing
and feature engineering
and selection
Model
development
Prediction
Deployment
Business
understanding and
hypothesis testing
Monitoring
Model management
Model accountability
Model
evaluation
Model
selection
Prediction bias
Bias, XAI & Drifts Data privacy
RAI definition Data privacy XAI & Model bias & privacy
Data bias
