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Turns Out Algorithms Are Racist
We are at a stage in technology where we need to make wide decisions of how we wish to learn from the past. Do we wish to imitate it or bring about a change. We need to challenge the inequalities that are present in the society. The first stage for this is the design process where the Data Scientist needs to understand which features are making the features bais. One should not let the black box decide and offers pronouncements and that we are encouraged to obey.
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Algorithms and bias: What lenders need to know
This article provides a guide on what banks need to consider when using AIs to make decisions regarding lending. It details several main points:
- AIs learn by using preexisting data that has the desired result already determined through manual means, which is subject to bias.
- While consumers can view their own credit report and check for its accuracy, there could be data from nontraditional sources which are not available to the consumer to check which lack transarency. This can even go as far as looking at social patterns, such as where a person shops or who they interact with.
- Recommendations are made that lenders monitor changing regulations and test for potential bias.
- Any feature in the algorithm should be carefully justified.
- Algorithms to examine bias in AI algorithms should be developed.
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Why AI is still waiting for its ethics transplant
This is an interview of Kate Crawford, a cofounder of AI Now, conducted by WIRED magazine. She is asked several questions which include the current state of ethics in AI, how AI developers need to both hire people outside of computer science to better understand social implications, and the state of government funding of ethics research under the Trump adminstration.
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The Dark Secret at the Heart of AI
This article goes into how, even though neural networks and deep learning algorithms have been designed by humans, humanity does not really know how these AIs work. They could use reason or use instinct like real people do. The inherent dangers in the unpredictability of AIs are summarized.
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How do machines learning algorithms learn bias?
An algorithm that has disparate impact is causing people to lose jobs, their social networks, and ensuring the worst cold start problem once someone has been released from prison. At the same time, people likely to commit crimes in the future are let to go free because the algorithm is blind to their criminality.
Machine Learning bias is caused by source data. Data munging is not fun, and thinking about sampling and outliers and population distributions of the training set can be boring, tedious work. Indeed, machines learn bias from the oversights that occur during data munging.
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Bias in software to predict future criminals - biased against blacks
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Causal models capture social biases and make clear the implicit trade-off between prediction accuracy and fairness in an unfair world.
When is your model fair?
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An algorithm is fair so long as any protected attributes A are not explicitly used in the decision-making process.
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An algorithm is fair if it gives similar predictions to similar individuals.
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A predictor Yˆ satisfies demographic parity if P(Yˆ |A = 0) = P(Yˆ |A = 1)
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A predictor Yˆ satisfies equality of opportunity if P(Yˆ = 1|A = 0, Y = 1) = P(Yˆ = 1|A = 1, Y = 1).
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How We Examined Racial Discrimination in Auto Insurance Prices
As AI becomes more and more complex, it can become difficult for even its own designers understand why it acts the way it does. A nationwide study by the Consumer Federation of America in 2015 found that predominantly African-American neighborhoods pay 70 percent more, on average, for premiums than other areas do. We analyzed aggregate risk data by zip code collected by the insurance commissioners of California, Illinois, Missouri and Texas from insurers in their states.
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They found that some insurers were charging statistically significantly higher premiums in predominantly minority zip codes, on average, than in similarly risky non-minority zip codes.
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Studies of auto insurance rates in Texas found that “drivers in poor and minority communities were disproportionately rejected by standard insurers and forced into the higher cost non-standard” insurance plans that are designed as a last resort for people who can’t otherwise buy insurance.
Similar statistics were shown in other states as well. Insurance companies fight that it is completely based not risk and they have removed racial features while training their model but demographies are the latent features that are strongly correlated with communities. These latent features bring bais in the model.
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How I'm Fighting Bias in Algorithms (Ted Talk)
What if you go to the washroom and the tap does not respond to your hands but it does for your your friend’s. Computer Vision uses images to recognize people and objects. But what if we train it with undiversified dataset? It will only respond to white skin because that is only what it has seen. There have been reported indices when a webcam is not able to detect black faces.[1] Not only this, image recognition softwares have been associating cleaning or the kitchen with women, for example and sports with men.
There needs to be a system that audits the existing software for impact that it has on minorities. There is a need to diversify our data. You can report discrimination that you’ve faced while using AI
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AI NOW is a research center based out of New York University focusing on the social implications of AI. Their research focuses on studying biases made by AIs that are used to make decisions related to housing, criminal justice, and employment. They also focus on determining the bias in datasets used to train AIs. Finally, they study how to safely integrate AIs into algorithms used by critical infrastructures.
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The AI NOW report summaries several recommendations made by AI NOW in the implementation of AI algorithms for making decisions in hiring, housing, as well as addressing biases in AI research itself. The report also contains a comprehensive literature review summarizing recent work in studying the social implications of AI.
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DeepMind Ethics Research Group - Google
DeepMind's Ethics & Society is a research unit within DeepMind that aims to explore and better understand the real-world
impacts of AI. It aims to help technologists put ethics into practice and help society anticipate and control the effects of AI, by enlisting some questions around:- privacy
- transparency
- fairness
- governence and accountability that should be addressed throughout the life cycle of projects.
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Why We Launched DeepMind Ethics & Society
Quoting DeepMind's Ethics webpage:
"The development of AI creates important and complex questions. Its impact on society—and on all our lives—is not something that should be left to chance. Beneficial outcomes and protections against harms must be actively fought for and built-in from the beginning. But in a field as complex as AI, this is easier said than done"
DeepMind being the world leader in artificial intelligence research and its application for positive impact, provides clear motivation and need to include ethics in AI. It is imperative to have similar ethics groups working hand in hand with the development teams in all organizations.
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Responsible Data Science is a novel collaboration between principal scientists from 11 universities and research institutes in the Netherlands. RDS aims to provide the technology to build in fairness, accuracy, confidentiality, and transparency in systems thus ensuring responsible use without inhibiting the power of data science. They conduct seminars and workshops (so far within Netherlands) and the material is available on their webpage as well.
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Fairness, Accountability, and Transparency in Machine Learning
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This is an article about how there needs to be increased transparency and accountability when it comes to examining data used by algorithms. A case of where an algorithm detecting the risk of criminal recidivism was biased against black defendants is established as an example for such a need. The article goes over efforts by the European Union to regulate such algorithms as well as recommendations from the White House on holding such algorithms accountable.
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The IEEE has published standards for the ethical application of AI. They are group into these broad categories covering social, privacy, and military impacts:
- Executive Summary
- General Principles
- Embedding Values Into Autonomous Intelligent Systems
- Methodologies to Guide Ethical Research and Design
- Safety and Beneficence of Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI)
- Personal Data and Individual Access Control
- Reframing Autonomous Weapons Systems
- Economics/Humanitarian Issues
- Law