Adriano Koshiyama

Department of Computer Science, University College London

PhD Student

Gower Street

London, London WC1E 6BT

United Kingdom

  • Department of Computer Science, University College London

SCHOLARLY PAPERS

12

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Scholarly Papers (12)

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1.

Algorithms in Future Capital Markets

Number of pages: 23 Posted: 11 Mar 2020 Last Revised: 13 May 2020
Adriano Koshiyama, Nick Firoozye and Philip Treleaven
Department of Computer Science, University College London, UCL - Computer Science and University College London
Downloads 555 (52,987)

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Finance, Artificial Intelligence, Machine Learning, Algorithms, Review

2.

A High-Level Overview of AI Ethics

Number of pages: 18 Posted: 18 Jun 2020
Emre Kazim and Adriano Koshiyama
University College London and Department of Computer Science, University College London
Downloads 107 (273,343)

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Artificial Intelligence, Governance, Ethics, Philosophy, Regulation

3.

Optimal Dynamic Strategies on Gaussian Returns

Number of pages: 32 Posted: 02 Jun 2019
Nick Firoozye and Adriano Koshiyama
UCL - Computer Science and Department of Computer Science, University College London
Downloads 76 (339,089)

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Algorithmic Trading, Dynamic Strategies, Over-Fitting, Quantitative Finance, Signal Processing

4.

A Review of the ICO’s Draft Guidance on the AI Auditing Framework

Number of pages: 15 Posted: 25 Jun 2020
Emre Kazim and Adriano Koshiyama
University College London and Department of Computer Science, University College London
Downloads 71 (352,293)

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5.

A Derivatives Trading Recommendation System: the Mid-Curve Calendar Spread Case

Number of pages: 43 Posted: 11 Nov 2018
Adriano Koshiyama, Nick Firoozye and Philip Treleaven
Department of Computer Science, University College London, UCL - Computer Science and University College London
Downloads 71 (352,293)

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Trading Recommendation System; Machine Learning; Derivatives, Swaptions

6.

Lack of Vision: A Comment on the EU’s White Paper on Artificial Intelligence

Number of pages: 8 Posted: 15 Apr 2020
Emre Kazim and Adriano Koshiyama
University College London and Department of Computer Science, University College London
Downloads 69 (357,791)

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AI, EU, Europe, European Commission, Legislation, Ethics, Artificial Intelligence, Innovation, Audit, Compliance

7.

No AI Regulator: An Analysis of Artificial Intelligence and Public Standards Report (UK Government)

Number of pages: 4 Posted: 24 Mar 2020
Emre Kazim and Adriano Koshiyama
University College London and Department of Computer Science, University College London
Downloads 48 (425,472)
Citation 2

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Policy, AI, Regulation, Data, Law

8.

The Interrelation Between Data and AI Ethics in the Context of Impact Assessments

Number of pages: 6
Emre Kazim and Adriano Koshiyama
University College London and Department of Computer Science, University College London
Downloads 34

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AI, Ethics, Data Ethics, Data Protection, Audit, Impact Assessment, AI Ethics, Artificial Intelligence

9.

Human Centric AI: A Comment on the IEEE’s Ethically Aligned Design

Number of pages: 32 Posted: 07 May 2020
Emre Kazim and Adriano Koshiyama
University College London and Department of Computer Science, University College London
Downloads 27 (519,501)

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IEEE, ethics, AI, Artificial Intelligence, Automation, Policy, Law, Affective Computing

10.

Impact Assessment Needed: An Analysis of Data Analytics and Algorithms in Policing (RUSI Report)

Number of pages: 6 Posted: 30 Mar 2020
Emre Kazim and Adriano Koshiyama
University College London and Department of Computer Science, University College London
Downloads 18 (574,662)
Citation 2

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Policy, AI, Algorithms, Data Analytics, Governance, Transparency, Explainability, Accountability, Policing, Ethics

11.

Data Ethics Principles: A Comment on the House of Lords Report ‘Regulating in a Digital World’

Number of pages: 6 Posted: 18 May 2020
Emre Kazim and Adriano Koshiyama
University College London and Department of Computer Science, University College London
Downloads 2 (687,870)

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House of Lords, Government, Governance, Policy, Regulation, RegTech, Law, Ethics, Artificial Intelligence

12.

Avoiding Backtesting Overfitting by Covariance-Penalties: An Empirical Investigation of the Ordinary and Total Least Squares Cases

The Journal of Financial Data Science Fall 2019, 1 (4) 63-83; DOI: https://doi.org/10.3905/jfds.2019.1.013
Posted: 02 Jun 2019
Adriano Koshiyama and Nick Firoozye
Department of Computer Science, University College London and UCL - Computer Science

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Algorithmic Trading, Overfitting, Covariance-Penalty, Total Least Squares, Ordinary Least Squares, Quantitative Finance