Adriano Koshiyama
University College London
Genetic programmingImpact assessmentAlgorithmMachine learningData miningFuzzy classificationTrading strategyEconometricsArtificial intelligenceTotal least squaresPolitical scienceAdaptive neuro fuzzy inference systemFuzzy control systemAuditSystematic tradingInterpretabilityMathematicsCorporate governanceComputer scienceGenetic representation
88Publications
7H-index
179Citations
Publications 80
Newest
#1Emre Kazim (UCL: University College London)H-Index: 3
#2Roseline Polle (UCL: University College London)
Last. Emine Yilmaz (UCL: University College London)H-Index: 27
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With the rapid adoption of algorithms in business and society there is a growing concern to safeguard the public interest. Researchers, policy-makers and industry sharing this view convened to collectively identify future areas of focus in order to advance AI standards - in particular the acute need to ensure standard suggestions are practical and empirically informed. This discussion occurred in the context of the creation of a lab at UCL with these concerns in mind (currently dubbed as UCL The...
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Business psychologists study and assess relevant individual differences, such as intelligence and personality, in the context of work. Such studies have informed the development of artificial intelligence systems (AI) designed to measure individual differences. This has been capitalized on by companies who have developed AI-driven recruitment solutions that include aggregation of appropriate candidates (Hiretual), interviewing through a chatbot (Paradox), video interview assessment (MyInterview)...
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#1Emre Kazim (UCL: University College London)H-Index: 3
Last. Adriano Koshiyama (UCL: University College London)H-Index: 7
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The publication of the EU’s draft AI legal framework is a milestone in the regulatory debate on AI. It proposes a risk based approach to regulating and reporting. In this white paper, we provide a high-level overview of the risk tiers, which we take to be the kernel of the legislation, and follow this by offering our initial thoughts and feedback on strategic points of contention in the legislation. Our main takeaways are: (i) Innovation - the sandbox approach may not be enough to ensure innovat...
1 CitationsSource
#1Emre Kazim (UCL: University College London)H-Index: 3
Last. Adriano Koshiyama (UCL: University College London)H-Index: 7
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This article is based on an archived draft version of the Information Commissioner's Office Guidance on the AI auditing Framework released for consultation in February 2020.
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#1Emre Kazim (UCL: University College London)H-Index: 3
#2Jeremy Barnett (UCL: University College London)H-Index: 2
Last. Adriano Koshiyama (UCL: University College London)H-Index: 7
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The Competition and Markets Authority (UK) has published their Research and analysis on ‘Algorithms: How they can reduce competition and harm consumers’. They have done so in parallel with a call for information on this critical area of research and potential regulation/standards. This article does two things: i. It provides a condensed summary of the report, and, ii. It notes our main findings and comments, which we offer as part of our feedback to the call for information. The principal takeaw...
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#1Emre Kazim (UCL: University College London)H-Index: 3
Last. Adriano Koshiyama (UCL: University College London)H-Index: 7
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As the use of data and artificial intelligence systems becomes crucial to core services and business, it increasingly demands a multi-stakeholder and complex governance approach. The Information Commissioner's Office’s ‘Guidance on the AI auditing framework: Draft guidance for consultation’ is a move forward in AI governance. The aim of this initiative is toward producing guidance that encompasses both technical (e.g. system impact assessments) and non-engineering (e.g. human oversight) componen...
9 CitationsSource
#2Adriano KoshiyamaH-Index: 7
Last. Philip TreleavenH-Index: 20
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This paper reviews the impact of data science and artificial intelligence (AI) on future ‘datadriven’ insurance markets. The impact of insurance automation (driven by so-called Black Swan1 events such as Covid-19) mirrors the impact of algorithmic trading that changed radically the capital markets (Koshiyama et al., 2020). The data science technologies driving change include: Big data, AI analytics, Internet of Things, and Blockchain technologies. These technologies are important since they unde...
2 CitationsSource
#1Adriano Koshiyama (UCL: University College London)H-Index: 7
#2Emre Kazim (UCL: University College London)H-Index: 3
Last. Elizabeth Lomas (UCL: University College London)H-Index: 3
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Business reliance on algorithms are becoming ubiquitous, and companies are increasingly concerned about their algorithms causing major financial or reputational damage. High-profile cases include VW’s Dieselgate scandal with fines worth of 34.69B, Knight Capital’s bankruptcy (~50M) by a glitch in its algorithmic trading system, and Amazon’s AI recruiting tool being scrapped after showing bias against women. In response, governments are legislating and imposing bans, regulators fining companie...
8 CitationsSource
#2Emre Kazim (UCL: University College London)H-Index: 3
Last. Adriano Koshiyama (UCL: University College London)H-Index: 7
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Spanish Abstract: Por cuenta de la pandemia las firmas tecnologicas y gobiernos estan en conversaciones sobre la creacion de muchas formas de coletas de datos, incluso los georeferenciados e de salud para hacer monitoreo de lockouts bloqueos, cuarentenas, aislamiento y distancia social. Los datos de salude, localizacion, compras, transferencias de dinero, biometria facial, certificados digitales pueden si ser una arma poderosa para combate del virus. Sin embargo si los datos colectados y paramet...
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#1Danielle Mendes Thame Denny (Fundação Armando Alvares Penteado)H-Index: 2
#2Emre Kazim (UCL: University College London)H-Index: 3
Last. Adriano KoshiyamaH-Index: 7
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Por cuenta de la pandemia las firmas tecnologicas y gobiernos estan en conversaciones sobre la creacion de muchas formas de coletas de datos, incluso los georeferenciados e de salud para hacer monitoreo de lockouts bloqueos, cuarentenas, aislamiento y distancia social. Los datos de salude, localizacion, compras, transferencias de dinero, biometria facial, certificados digitales pueden si ser una arma poderosa para combate del virus. Sin embargo si los datos colectados y parametrizados no fueren ...
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