Dr Nitika Bhalla is a Project Officer De Montfort University, working specifically in theCentre for Computing and Social Responsibility. She is working on managing EU funded proposals and projects with various European stakeholders.
Dr Nitika Bhalla is a Project Officer De Montfort University, working specifically in theCentre for Computing and Social Responsibility. She is working on managing EU funded proposals and projects with various European stakeholders.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme, under grant agreement No 786641.
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Related to the potential to favour people with more money to access SIS (i.e. poorer people may not be able to afford access or the knowledge to access these technologies), at the local national or even global level
Related to the need to explain and justify one’s decisions and actions to its partners, users and others with whom the SIS interacts; Regarding liability, it is related to the sense that a person who has suffered loss because of a decision made by SIS may be owed a duty of care
Related to using misrepresentative data or misrepresenting information (ie. predictions are only as good as the underlying data) and how that affects end user views on what decisions are made (i.e. whether they trust the SIS and outcomes arising from it)
Related to the possibility of misinterpreting data, implementing biases, and diminishing the accuracy of SIS recommendations
Related to the samples people that might be chosen/involved in generating data
The degree to which people perceive they or the SIS are in control
The degree to which all involved feel they have an equal say in the outcomes, compared with the SIS
Related to discrimination in terms of who has access to data. For example, discrimination in algorithms may be conscious or unconscious acts by those employing the SIS, or a result of algorithms mirroring society by reflecting pre-existing biases
Related to the potential for SIS to boost economic growth and productivity, but at the same time creating equally serious risks of job market polarisation, rising inequality, structural unemployment and emergence of new undesirable industrial structures
Related to how data is collected and manipulated (ie. how it is used), also who has access to the data and what they might do with it as well as how resources (e.g. Energy) might be distributed according to the guidance arising out of the data
Related to the manipulative power of algorithms results in nudges towards some preferred behaviours, free will and the self-determination of people, which are the preconditions for democratic constitutions, run the risk of being compromised
The use of SIS to monitor an individual’s health and how much control one can have over that
The potential for SIS to reduce the contact between people, as they take on more of the functions within a society
Related to the potential for SIS to favour people with more money (i.e. poorer people may not be able to afford access or the knowledge to access these technologies)
The level at which SIS is seen as impacting on the dignity and care for older people, for example how much a care robot might exert power over an older person’s life and ‘tell them what to do’
Concerns over the potential use of SIS for both military and non-military use
Related to the use of SIS resources contributing to the production of greenhouse emissions as well as impacting the environments they are built on
Related to how algorithms used in SIS affect how people analyse the world and modify their perception of the social and political environment
Related to the digital divide and the potential for SIS to favour people with more money (i.e. poorer people may not be able to afford access or the knowledge to access these technologies), at the local national or even global level; also related to discrimination in terms of who has access to data
Related to informed consent being difficult to uphold in SIS when the value and consequences of the information that is collected is not immediately known by users and other stakeholders, thus lowering the possibility of upfront notice
The internal integrity of the date used as well as the integrity of how the data is used by a SIS
The use of SIS within judicial systems, for example AI used to ‘inform’ judicial reviews in areas such as probation
Where ownership of data sits, and how transparent that is, for example when you give details to an organisation, who then ‘owns’ the data, you or that organisation
What is done with and to the data, for example when used with other data points to make a dataset, how is this done, what basis and who is making sure that it is not in some way abused
Related to the use of SIS to make predictions about future possible military, criminal and malicious scenarios that can elaborate and improve strategies for instance, in cyber-attacks and cyber espionage
Related to the fact that the knowledge offered by SIS and its practices, and how to regulate this knowledge is in the hands of a few powerful corporations
Related to how much data is collected, where from (i.e. public such as social media or privately directly from the person/home) and how well it is looked after
Related to the role of people themselves and to the capability of SIS to answer for one’s decision and identify errors or unexpected results
As SIS, such as AI, gain more complexity and empowerment, then to what degree they should have rights and be protected, eg. digital personhood
Related to the sensitivity of SIS given the amounts and kind of data that they hold which needs protection of the systems against hackers to ensure a positive impact and reduce risks
Related to a concern about the data centres needed to run SIS, as the demand for huge computing power along with greater resources and energy required for data collection, storage and analytics
Related to the need to describe, inspect and reproduce the mechanisms through which SIS make decisions and learns to adapt to its environment, and to the governance of the data used created.
Related to using misrepresentative data or misrepresenting information (i.e. predictions are only as good as the underlying data) and how that affects end user views on what decisions are made (i.e. whether they trust the SIS and outcomes arising from it); also related to informed consent and that helps with trust
The worry that use of SIS will lead to significant drop in the need to employ people
The concerns over how SIS might use your and anyone’s personal data
