A presentation at The Conf in in São Paulo, State of São Paulo, Brazil by Carla Vieira
The Artificial Intelligence “Black-box” Carla Vieira @carlaprvieira Ilustração: Hanne Mostard
About me Carla Vieira Information Systems – USP Artificial Intelligence Evangelist Community Manager perifaCode @carlaprvieira @carlaprv@hotmail.com
Tech Conferences
data bias privacy ethics law
We need to talk less about Artificial Intelligence hype … … and more about how we are using this technology.
#1 Google Photos
#2 Gender Shades Article http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf
“Whether AI will help us reach our aspirations or reinforce the unjust inequalities is ultimately up to us.” Joy Buolamwini
#3 Tweet
#4 Google’s Algorithm
• 46% false positives for African American • African American authors are 1.5 times more likely to be labelled “offensive” https://homes.cs.washington.edu/~msap/pdfs/sap2019risk.pdf
#5 COMPAS Software Software COMPAS
https://www.research.ibm.com/artificial-intelligence/trusted-ai/diversity-in-faces/
Artificial Intelligence needs to learn from the real world. Creating a smart computer is not enough, you need to teach it the right thing. https://about.google/stories/gender-balance-diversity-important-tomachine-learning/?hl=pt-BR
Gender Gap in Artificial Intelligence “Only 22% of AI professionals globally are female, compared to 78% who are male.” (The Global Gender Gap Report 2018 - p.28)
Bias Human Bias Technology
Even though these decisions affect humans, to optimize task performance ML models often become too complex to be intelligible to humans: black-box models
INPUT BLACK BOX OUTPUT
INPUT BLACK BOX OUTPUT
JUSTICE MATH
“This new law is a complete shame for our democracy.” Louis Larret Chahine Co-founder PREDICTICE https://www.artificiallawyer.com/2019/06/04/france-bans-judge-analytics-5-years-in-prison-for-rule-breakers/
https://edition.cnn.com/2019/05/14/tech/san-francisco-facial-recognition-ban/index.html
How to open this black-box? EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI) TRANSPARENCY TRUST
XAI intends to create a new suite of ML techniques that produce more interpretable ML models
Accuracy vs. Interpretability trade-off
Explainability Pre-modelling explainability Explainable modelling Goal Understand/describe data used to develop models Goal Develop inherently more explainable models Methodologies • Exploratory data analysis • Dataset description standardization • Dataset summarization • Explainable feature engineering Methodologies • Adopt explainable model family • Hybrid models • Joint prediction and explanation • Architectural adjustments • Regularization Post-modelling explainability Goal Extract explanations to describe pre-developed models Methodologies • Perturbation mechanism • Backward propagation • Proxy models • Activation optimization
Post-modelling explainability The proposed taxonomy of the post-hoc explainability methods including the four aspects of target, drivers, explanation family, and estimator.
Post-modelling explainability first a perturbation model is used to obtain perturbed versions of the input sequence. Next, associations between input and predicted sequence are inferred using a causal inference model. Finally, the obtained associations are partitioned and the most relevant sets are selected.
http://www.portaltransparencia.gov.br/download-de-dados
If we want AI to really benefit people, we need to find a way to get people to trust it.
https://serenata.ai/ https://brasil.io/home https://colaboradados.github.io/
Less machines that are going to take our jobs and more about what technology can actually achieve…
The choices we are making today about Artificial Intelligence are going to define our future.
Thank you! Carla Vieira @carlaprvieira carlaprv@hotmail.com
Useful links − AI NOW − Racial and Gender bias in Amazon Rekognition − Diversity in faces (IBM) − Google video – Machine Learning and Human Bias − Visão Computacional e Vieses Racializados − Machine Bias on Compas − Machine Learning Explainability Kaggle − Predictive modeling: striking a balance between accuracy and interpretability
Useful links −Racismo Algorítmico em Plataformas Digitais: microagressões e discriminação em código −Metrics for Explainable AI: Challenges and Prospects −The Mythos of Model Interpretability −Towards Robust Interpretability with Self-Explaining Neural Networks −The How of Explainable AI: Post-modelling Explainability
As humans we’re inherently biased. Sometimes it’s explicit and other times it’s unconscious, but as we move forward with technology how do we keep our biases out of the algorithms we create? We need to have a conversation about how AI ‘Black Box’ should be governed and ask who is responsible for overseeing the ethical standards of these algorithms; and the need for gender balance — having access to large and diverse points of view helps to train algorithms to maintain the principle of fairness.
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