The Frontiers of Machine Learning 2017 Raymond and Beverly Sackler U.S.-U.K. Scientific Forum (2018) / Chapter Skim
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2 Machine Learning Challenges
2 Machine Learning Challenges
Pages 11-19
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From page 11... ...
Medical researchers, for example, have begun to use genetics data, medical records, patient registries, activity logs, environmental data, and medical imaging to better understand all aspects of human health. In the field of genomics, modifications in gene regulation and gene function resulting from mutated segments of DNA allow researchers to identify which regions of the genome influence a particular trait associated with a particular disease.
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From page 12... ...
Government plays an important role as well through research funding and through its involvement in deploying infrastructure technology, such as traffic light sensors, that will affect the behaviors of automated vehicles. While technical solutions can address the broader social and ethical implications of this technology, collaborations also contribute to addressing these challenges.
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From page 13... ...
Curricular changes will not happen overnight, but conversations among interested stakeholders should begin and plans should be made to fund programs that could improve students' chances of securing relevant work in the future. Infrastructure In addition to curriculum changes, modifications to university course materials and course content can help prepare both machine learning experts and those likely to work with machine learning systems in the future: • Technical programs will need to update the skills and knowledge with which they equip students to work in specific fields.
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From page 14... ...
The advanced analytical capabilities offered by machine learning pose new challenges to managing privacy: in some applications, machine learning will use data containing sensi tive information, while in other cases machine learning might create sensitive insights from seemingly mundane data. New privacy-preserving technologies (e.g., The advanced analytical de-identification of data, differential capabilities offered by privacy, homomorphic encryption)
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From page 15... ...
The EU General Data Protection Regulation, which is expected to go into effect in May 2018, protects data that expose racial or ethnic origin; political or religious beliefs; and genetic, biometric, and health data.7 This is not the case for all existing regulatory instruments: unlike the previously mentioned regulated scores, the unregulated Alternative Credit Score in the United States can use factors that would normally be prohibited under the Fair Credit Reporting Act because it uses those factors for marketing financial products instead of for determining creditworthiness.8 Unregulated data includes health data not subject to the Health Insurance Portability and Accountability Act of 1996 (HIPAA) ,9 transactional data, historic data sets, and commercial data sets.
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From page 16... ...
All companies need to be vigilant in preparing for or preventing these situations, perhaps by drawing on the expertise of hackers.10 Trust, Transparency, and Interpretability "Artificial Intelligence and Life in 2030" suggests that "well-deployed artificial intelligence predic tion tools have the potential to provide new kinds of transparency about data and inferences, and may be applied to detect, remove, or reduce human bias, rather than reinforcing it." 11 A key barrier to achieving transparency in artificial intelligence tools is the lack of a com mon language, as varying definitions of "transparency," "explainability," and "interpretability" exist across disciplines. Additionally, there are various lenses through which these concepts can be considered.
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From page 17... ...
Scoring systems applied to predict the likelihood of repeat offending are increasingly data-driven, and some make use of machine learning. These systems offer the hope of reducing bias in criminal sentencing, if the algorithm being used can be designed in a way that supports advanced analysis free of societal assumptions about factors such as race, gender, or socioeconomic status.
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From page 18... ...
Living Alongside Machine Learning In addition to specific challenges arising from the governance of data used in machine learning or the capabilities of current machine learning systems, and the benefits to be gained from such systems, there is a broader suite of questions raised by the increasing pervasiveness of machine learning systems. At a fundamental level, these questions ask how society will change as people live and work with automated sys tems, as well as with the new forms of human–computer interaction that How will society change as could follow.
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From page 19... ...
Will humans today be able to adapt similarly to the changes to work and other aspects of life resulting from advances in machine learning? Questions about how people live alongside machine learning will persist, and continuing dialogue will be important in negotiating them.
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