IBM Research AI Selected Publications 2018

191 Pages·2017·32.61 MB·English

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IBM Research AI
Selected Publications
2018
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Trusting AI __ Contents
Introduction 004
Chapter 1
Fairness 006
Chapter 2
Explainability 082
Chapter 3
Robustness 184
Chapter 4
Value Alignment 258
Chapter 5
Transparency and Accountability 284
Chapter 6
AI for Social Good 328
Bibliography 378
Artificial intelligence TrIn
usting AItroductio
n
systems are
increasingly being
used to support human decision-making. AI can decisions that matter will require much more than and then outline diverse approaches for how
help doctors make sense of millions of patient the proof of accuracy and rigorous testing. To trust to achieve fairness, robustness, explainability,
records, farmers determine exactly how much a decision made by an algorithm, we need to know accountability and value alignment and, most
water each individual plant needs and insurance that it is reliable and fair, that it can be accounted importantly, how to integrate them throughout the
companies assess claims faster. And while AI for, and that it will cause no harm. We need entire lifecycle of an AI application. “Do no harm,”
holds the promise of delivering valuable insights assurance that it cannot be tampered with and that “be honest and trustworthy,” “be fair and do not
and knowledge across a multitude of applications, the system itself is secure. We need to understand discriminate” – it is the universal code of ethics
broad adoption of AI systems will rely heavily on the rationale behind the algorithmic assessment, that distinguishes humans from other species.
the ability to trust their output. recommendation or outcome, and be able to In our research, we aspire to create AI solutions
Human trust in technology is based on our interact with it, probe it – even ask questions. And that rise to such expectations. Another universal
understanding of how it works and our assessment we need assurance that the values and norms of ethical principle is to perform good works for
of its safety and reliability. We drive cars trusting our societies are also reflected in those outcomes. society and contribute to our well-being. Hence,
that the brakes will work and we undergo laser Fairness, robustness, explainability, accountability we conclude this chapter by showcasing a range of
eye surgery trusting the laser to make the right and value alignment are the underpinnings innovative AI applications toward addressing social
movements. In both cases, trust comes from of trusted AI. Moving forward, “build for and humanitarian challenges. Promoting and
confidence that the system will not make a mistake performance” will not suffice as an AI design advancing such usage is as important as arming AI
thanks to extensive training, exhaustive testing, paradigm. We must learn how to build, evaluate for trust, and it is our responsibility to pursue them
experience, safety measures, standards, best and monitor for trust. both -- as technologists, lawmakers, policymakers
practices and consumer education. While the In this collection of papers we explore how to and members of society. Only then we will achieve
005
principles of safety design are essential in building do that. We start by defining the dimensions of a broad adoption of AI and enable solutions that
AI systems, they are not enough; entrusting AI with trusted AI as scientific and engineering objectives, truly represent the future of this technology.
1 Fairness
Fairness Selected
Publications
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“On two occasions I have The explosion of digital information coupled with 1 __ AI Fairness 360: An Extensible Toolkit for
been asked, ‘Pray, advances in machine learning drive today’s AI Detecting, Understanding, and Mitigating Unwanted
Mr. Babbage, if you put renaissance. Computers now have the ability Algorithmic Bias
into the machine wrong to learn, detect patterns, and inform decisions. p __ 010
figures, will the right Yet, whether we want to acknowledge it or not, we
answers come out?’ ” live in a world that contains biases and prejudices, 2 __ Automated Test Generation to Detect Individual
and these biases can easily enter AI systems Discrimination in AI Models 
Charles Babbage through training data we create, collect or process. p __ 032
The systems pick them up, encode them, and have
the potential to scale them. 3 __ Data Pre-Processing for Discrimination
Prevention: Information-Theoretic Optimization
This is why instrumenting AI for fairness is and Analysis
essential. Properly calibrated, AI could assist p __ 042
humans in making more informed choices,
process and evaluate facts faster and better, or 4 __ Analyze, Detect and Remove Gender Stereotyping
allocate resources more fairly -- giving us an from Bollywood Movies
opportunity to break the chain of human biases. p __ 058
This section highlights our work toward creating 5 __ Towards Composable Bias Rating of AI Systems
fair and just AI systems. We present a diverse p __ 074
selection of papers that address bias mitigation
across the entire lifecycle of an AI application,
as well as across different bias and data types,
including individual techniques to detect and
mitigate bias in datasets and models [1,2],
a comprehensive bias mitigation and education
toolkit [3], understanding and removing gender
stereotypes [4] and rating AI services for bias [5].
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