Recent developments in data science ethics over the last number of months have shown that support and guidance for professionals has become an increasingly important topic; the European Insurance and Occupational Pensions Authority (EIOPA) established a ‘Consultative Expert Group on Digital Ethics in Insurance’ in September last year, the Actuarial Association of Europe published a document outlining its current perspective on the ethics and responsibility of data scientists and the European Actuarial Academy is hosting its first ‘Data Science and Data Ethics Conference‘ on the 29th and 30th of June in 2020. Much of the activity has discussed the evolving role of actuaries in data science, noting the opportunities within the space but acknowledging that a number of risks still need to be addressed.
To assist in navigating some of the risks posed by the ethical considerations of data science, in October 2019 the Institute and Faculty of Actuaries (IFoA) and the Royal Statistical Society (RSS) Data Science Section jointly published ‘A Guide for Ethical Data Science.’ This guide aims to address the challenges faced by their members while complementing existing ethical and professional guidance. It is non-mandatory and does not impose any obligations upon RSS or IFoA members.
The development of the guide began in 2018
with four workshops with data science professionals. These workshops centred on
four key questions raised by the RSS and IFoA:
- What does a good data science
workflow look like?
- How should data science fit
into the structure of an organisation?
- What do executives and managers
need to know about data science?
- What is a data scientist’s
responsibility to wider society?
Although the data science practitioners found that best practice for data science is dependent on industry and even company-specific factors such as organisational design, historical workflows and the availability of skills within teams, they did broadly agree on several high-level principles and practices which have informed the guide. The guide does highlight, however, that some of the more complex issues faced by practitioners will require further thought and input from professionals.
The guide looks at five recurring ethical
themes from existing frameworks relating to data science and artificial
- Seek to enhance the value of data science for society.
- Avoid harm.
- Apply and maintain professional competence.
- Seek to preserve or increase trustworthiness.
- Maintain accountability and oversight.
Traditionally, insurers have relied heavily on data they have collected as well as industry-specific data to inform their business decisions and strategy. However, data science techniques have become more sophisticated, allowing insurers to better understand the relationship between internal and external data sources. Predictive analytics, machine learning, data mining, and artificial intelligence are helping companies extract value from both sources.
In this article, Milliman’s Cormac Gleeson and Eamon Comerford discuss how the use of external data can complement a company’s wider data science initiatives. They also explore some of the challenges posed by working with external data.
Ireland is in the midst of an affordable housing crisis.
Figures published by the Department of Housing, Planning and Local Government
show 6,497 homeless adults in late 2019 with a further 3,778 homeless children.
In total, 1,721 families were in emergency or temporary accommodation including
hotels, bed and breakfasts, hostels, and other temporary accommodation facilities.
In addition, a large number of people are in private rental accommodation,
relying on local authority assistance in paying rent.
A shortage of housing supply seems to be at the crux of the
problem, particularly in the context of increased demand arising from improved
economic conditions and an increased number of large multinational employers. Harnessing
the power of pooled investment funds could help alleviate this crisis while
also potentially providing returns to individual investors.
The proposed pooled investment fund would:
- Build a portfolio of residential properties,
through acquisition and/or development. Initially, it is likely that the focus
of the fund would be on purchasing residential properties, but development of
suitable residential properties would also be possible over time.
- Rent those properties on long-term secure
tenancies with transparent rules around rental increases either to tenants
directly in receipt of the Housing Assistance Payment or directly to local
authorities to supplement the local authority housing stock.
In this paper, Milliman consultants discuss the rationale for a pooled investment fund focused on social and affordable housing.
Organisations completing their second full year of Solvency II reporting are required to submit four additional reporting templates. These additional templates disclose the change in the excess of assets over liabilities over the 12-month period since the previous set of annual reporting templates were submitted to regulators. In this briefing, Milliman’s Barry Murphy and Cormac Gleeson discuss these templates and provide insight on how to approach them.