AI & Data Analytics, Privacy, Ethics & Integrity

Stratosphere has decades of experience of the equity capital markets debates on algorithmic and high frequency trading, the technology “space race” for latency, co-location, speed and data analytics processing power in the capital markets. Policy makers and regulators focused on the potential of algorithmic trading to confer unfair advantage or distort markets and price formation.

Next generation machine learning, predictive analytics and artificial intelligence technologies have great promise, but bring new challenges, including demands for them to be controllable and auditable. Data and platform-based operators face new anti-trust theories of competitive harm, even at start-up level. Stratosphere is engaging with governments, regulators, think tanks and other entities looking at codes of ethics around computational integrity and use of data.

Topics

The use of data analytics and algorithms is not new; but the acceleration of these technologies as well as machine learning and artificial intelligence is generating opportunity and threat assessments by states, corporates, regulators and society.

Policy makers are focused on issues including

The role of the state:

  • As a large holder of public data
  • To regulate private sector data holders
  • Using data to exert control over elements of society
  • The Macron government in France recently announced that all algorithms used by the state must be published so that the public can verify their integrity.

The role of companies:

  • Abuse of data collection by companies
  • Computational integrity of algorithms; consequences of algorithm or AI errors, or inbuilt biases
  • Proposals to ensure auditability of algorithms and other forms of autonomous decision making technology
  • GDPR requirement for data controllers using algorithms for automated decision making to explain the logic of the decisions, and use “appropriate mathematical or statistical procedures” to avoid or reduce the risk of errors and inaccuracies (Recital 71 GDPR)

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