Big Data – A Tool for Inclusion or Exclusion?
Publié le 20 avril 2018
We are in the era of big data. With a smartphone now in nearly every pocket, a computer in nearly every
household, and an ever-increasing number of Internet-connected devices in the marketplace, the amount of
consumer data flowing throughout the economy continues to increase rapidly.
The analysis of this data is often valuable to companies and to consumers, as it can guide the
development of new products and services, predict the preferences of individuals, help tailor services and
opportunities, and guide individualized marketing. At the same time, advocates, academics, and others have
raised concerns about whether certain uses of big data analytics may harm consumers, particularly low-
income and underserved populations.
To explore these issues, the Federal Trade Commission (“FTC” or “the Commission”) held a public
workshop, Big Data: A Tool for Inclusion or Exclusion?, on September 15, 2014. The workshop brought
together stakeholders to discuss both the potential of big data to create opportunities for consumers and
to exclude them from such opportunities. The Commission has synthesized the information from the
workshop, a prior FTC seminar on alternative scoring products, and recent research to create this report.
Though “big data” encompasses a wide range of analytics, this report addresses only the commercial use
of big data consisting of consumer information and focuses on the impact of big data on low-income and
underserved populations. Of course, big data also raises a host of other important policy issues, such as
notice, choice, and security, among others. Those, however, are not the primary focus of this report.
As “little” data becomes “big” data, it goes through several phases. The life cycle of big data can be
divided into four phases: (1) collection; (2) compilation and consolidation; (3) analysis; and (4) use.
This report focuses on the fourth phase and discusses the benefits and risks created by the use of big data
analytics; the consumer protection and equal opportunity laws that currently apply to big data; research in
the field of big data; and lessons that companies should take from the research. Ultimately, this report is
intended to educate businesses on important laws and research that are relevant to big data analytics and
provide suggestions aimed at maximizing the benefits and minimizing its risks.
CATÉGORIESTechniques algorithmiques Types de systèmes algorithmiques Apprentissage (machine learning): supervisé (prédictif) ou non-supervisé Biais des données et des algorithmes Biais éthiques et juridiques Biais techniques (Evaluation expérimentales, vérification de code, véracité des données, etc) Questions sociales, éthiques et juridiques Bulle filtrante Classement (ranking) Discrimination préjudiciable/illicite, discrimination souhaitée/légale Domaines d'application Explicabilité des algorithmes Information flow monitoring Justice Marketing et Publicité Moteurs de recherche Provenance des données et contrôle d'usage des données Recommandation Responsabilité, redevabilité