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SDE Strategy Guide
The Systemic Data Ethics Guide
The Guide is a work in progress. Created from distilled, aggregate information from multiple sources, this represents an example of the kind of insight that a structured franework makes possible.
The Systemic Data Ethics Guide
The Systemic Data Ethics framework is a stable, whole system, view of data ethics. Instead of defining principles, it establishes a set of 12, interconnected, areas of practice called “domains”.
Select a domain to explore (Strategy is the only complete option right now)
level
Intention
Implementation
Information
Implication
Governance
Strategy
Accountability
Transparency
Sustainability
Operations
Fairness
Infrastructure
Application
Consequences
Design
Agency
Materials
Compliance
Inclusivity
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Strategy
Strategy
The organization’s principles, and it’s approach to the opportunity and risks related to using and commercialising data.
The organization’s principles, and it’s approach to the opportunity and risks related to using and commercialising data.
Systemic framework attributes
Within the Systemic Data Ethics framework, the domain is defined by combining these different attributes.
Strategy
Level: Governance
Governance
The work required at an organizational level to use and manage data and AI within an economic or social context. This includes a business decisions and it’s strategy to commercialise data, an organization’s structure, the way in which it communicates (and listens to) the public as well as the long term, social and environmental impact of any data or AI use.
(Matrix rows)
Dimension: Intention
Intention
The ethical decisions required to achieve goals and objectives, at an individual/design level, day-to-day operations and strategy.
(Matrix columns)
Overview
The strategy domain is concerned with the overall direction and purpose of an organization - and how this defines the underlying ethical context for all other work. The key data ethics activities in this domain provide high level guidance, aligning and balancing the, possibly competing, desires to generate profit with the benefits, and limitations, of ethical behaviour.
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Tags
legitimate interestespurposeimproper userisksdata practicescultural valuescompliancelawethicsfuture-proofpublic trustbenefitAIchangetechnologyengagementmisconductwork environmentresponsibilitygovernancedecentralisationresponsible innovationanticipationreflexivitybehaviourresiliencefeedback loopsmanagementdisagreementalignment Show 7 more
Where to start
First steps
Document and communicate the positive value of data ethics in your organization.Define a set of data ethics principles.Know the purposes for collecting and the value you seek to deliver with data.
Compliance actions
Document your “legitimate interests”
Relevant regulations
GDPR
Understanding the Strategydomain
Strategy
Ethical challenges
What are we willing to sacrifice for our principles and values?
It’s all very well shouting about our values, but, when it comes down to a real decision between principles or profit.... at which point do we choose to take the money?
When something does go wrong, how will we act?
Assume that you will miss something, and that a data breach or ethical failure will happen, no matter how well you prepare. How will your organization respond?
Are we choosing to build tools that could be used to harm people or society?
How clearly can we articulate the commercial value of data ethics to our business?
How will we respond in the event of a major data breach?
Do we know what “ethical data use” looks like in our sector/industry?
The big questions to answer
How does data, and it’s ethical use, relate to our business model and commercial interests?How do we provide guidance to our staff on what ethical conduct and data use looks like in our context?What impact will technology and data have on the way in which we do business and make decisions?How do does an organization successfully evolve to integrate data ethics at it’s core?How do we approach the uncertainty and risks related to working and innovating with data and AI?How does data ethics impact our business ecosystem?
The primary benefits
Employee engagementCompetitive advantageIncreased public trust
Further development
Actions
Document and communicate the positive value of data ethics in your organization.Define a set of data ethics principles.Know the purposes for collecting and the value you seek to deliver with data.Establish a requirement to regularly review data purpose and need at every level. Articulate levels of risks and model your response to different situations
Projects
Establish a long term, iterative, data ethics transformation strategyDevelop your leadership’s ability to see how technology will change your businessCultivate ecosystem wide ethical standards
Considerations
Ensure that leadership are seen to embody your values and principlesData ethics is a culture shift, not a technology or compliance projectEstablish a frameworks for unknown risks
Dive into the detail
Our foundations go deep. Select a question from the list below to explore the key recommendations for the domain. Once selected, scroll past this table to read on.
Strategy
Select a question to dive into
Action: select big
Summary
Recommendations
sources
How does data, and it’s ethical use, relate to our business model and commercial interests?
There should always exist a clear purpose for any form of data collection. Any organization choosing to work with data must understand and communicate its reasons and purpose.
Big Data and AI Executive Survey 2021
Gov.uk Data ethics framework
ODI canvas
GDPR
Applying legitimate interests in practice
Data protection law and ethics
How do we provide guidance to our staff on what ethical conduct and data use looks like in our context?
Clearly defined and communicated principles and values form the foundation of all data ethics work. Identifying, refining, sharing and living these principles is vital to developing ethical data practices.
Big Data and AI Executive Survey 2021
2009 National Business Ethics Survey
Incentivizing Ethics
What impact will technology and data have on the way in which we do business and make decisions?
Every business must now learn to become data driven. The gap between the leaders and the laggards in adopting analytics is growing. Those that get left behind, will fall by the wayside.
Understanding what needs to change, and how to create the data-culture required is a vital aspects of data ethics.
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Data protection law and ethics
Developing a framework for responsible innovation
How do does an organization successfully evolve to integrate data ethics at it’s core?
Data ethics is a new lens on the way in which organizations operate. Developing an organization’s capabilities to work with data in an ethical manner can only be achieved as part of a broad transformational effort.
Big Data and AI Executive Survey 2021
Gov.uk Data ethics framework
How do we approach the uncertainty and risks related to working and innovating with data and AI?
Data ethics presents a new and complex risk landscape. Traditional risk management frameworks may not be sufficient to detect and address the emergent and unknown data ethics risks.
Guidelines for the Ethical Use of AI and Big Data Systems
Data protection law and ethics
Developing a framework for responsible innovation
How does data ethics impact our business ecosystem?
Successful data ethics, by necessity, must reach beyond individual organizations and into their ecosystem. Partners and sub-contractors must all adhere to the same standards, even competitiors may benefit from aligned frameworks.
Beijing AI Principles
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Recommendations
recommendation
detail
sources
Effort
Timescale
benefits
Identify and document your reasons for using any sensitive personal data
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Benefits
Benefit
Summary
Source
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Cultural behaviours
behaviour
description
Admit mistakes
Feel comfortable admitting when you’re are wrong and when things have not gone to plan.
Open to ideas
Don’t assume you know the answers before you understand the detail
Being allowed to fail
Feel that you are able to safely try something new, even if you are not sure if it will work.
Honesty
Be able to say the scary things
Transparency
Being open and accountable
Self responsibility
Taking responsibility for one’s actions
Social awareness
Being aware of the impact of actions on society
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Resources
Tools
Go to source
Description
Author/Organization
ODI canvas
The Data Ethics Canvas is a tool for anyone who collects, shares or uses data. It helps identify and manage ethical issues – at the start of a project that uses data, and throughout.
The ODI
Responsible Stakeholder Considerations - A Starter for Ten
Thinking more holistically about who may be impacted by technology design decisions can be the first step to creating more ethical and responsible innovations
Consequential
Applying legitimate interests in practice
The ICO’s guidelines for applying Legitimate Interests under the GDPR
ICO
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Sources
The Systemic Data Ethics structure allows us to categorise “aspects” of other data ethics frameworks and systems to build a big, whole system picture of each domain, and data ethics in general.
As we process each of our sources, we identify key sections of text, and assign each to a level and dimension. This process is, by definition, a little blurry. Ideas will often apply to multiple domains, but we are generally able to be sufficiently accurate.
Principle sources
These sources focus primarily on this domain.
Go to source
Description
Author/Organization
ASILOMAR AI PRINCIPLES
A set of AI principles defined at the 2017 Asilomar conference.
Future of life institute
AI in the UK: ready, willing and able? (An AI Code)
Transcripts from the House of Lords hearing on AI.
GOV UK
Incentivizing Ethics
Recommendations on incentivising ethics
Navex Global
Data protection law and ethics
A guide to GDPR from Trilateral research
Trilateral Research
2009 National Business Ethics Survey
Supplemental research from the 2009 National Business Ethics Survey
Ethics Resource Center
Developing a framework for responsible innovation
An academic framework for responsible innovation.
Jack Stilgoe,Richard Owen,Phil Macnaghten
A Way of Being
A profound and deeply personal collection of essays by renowned psychologist Carl Rogers The late Carl Rogers, founder of the humanistic psychology movement and father of client-centered therapy, based his life's work on his fundamental belief in the human potential for growth.
Carl Rogers
Integrity, business ethics and the resilient organisation
A vital aspect of resilience is an organisation’s ability to demonstrate integrity and embed ethical business through alignment of corporate purpose and personal values
PWC
What is ethical leadership
A summary of the importance of ethical leadership and the traits of ethical leaders
Western Governors University
ETHICS AT WORK An employer’s guide
A guide for people professionals so that they can define ethical behaviour, identify unethical behaviour, and take steps to create a shared ethical culture which avoids this type of behaviour.
Chartered Institute of Personnel and Development
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Secondary sources
These sources all have aspects relevant to this domain
Source document
Author/Organization
Gov.uk Data ethics framework
GOV UK
Jessica Morley, Luciano Floridi, Libby Kinsey, Anat Elhalal
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Aspect Word cloud 3
Domain principles and values 3
title
citation
source
Sponsor an organisation-wide approach
Institutions should drive data ethics from the top and ensure it is adopted across business functions by building it into their existing governance frameworks
Establish appropriate senior sponsor-driven enterprise governance frameworks
Senior leaders need to be evangelical in their support for ethical data use, but they must also ensure robust day-to-day behaviours
Big Data and AI Executive Survey 2021
AI Arms Race
An arms race in lethal autonomous weapons should be avoided.
ASILOMAR AI PRINCIPLES
Risks
Risks posed by AI systems, especially catastrophic or existential risks, must be subject to planning and mitigation efforts commensurate with their expected impact.
ASILOMAR AI PRINCIPLES
Harmony and Cooperation
Cooperation should be actively developed to establish an interdisciplinary, cross-domain, cross-sectoral, cross-organizational, cross-regional, global and comprehensive AI governance ecosystem, so as to avoid malicious AI race, to share AI governance experience, and to jointly cope with the impact of AI with the philosophy of "Optimizing Symbiosis".
Beijing AI Principles
Anticipation
The call for improved anticipation in governance comes from a variety of sources, from political and environmental concerns with the pace of social and technical change (e.g. ), to scholarly (and latterly, policy) critiques of the limitations of top-down risk-based models of governance to encapsulate the social, ethical and political stakes associated with technoscientific advances.
Much of the academic literature here makes the point that successful anticipation also requires understanding of the dynamics of promising that shape technological futures
Developing a framework for responsible innovation
Reflexivity
Reflexivity, at the level of institutional practice, means holding a mirror up to one's own activities, commitments and assumptions, being aware of the limits of knowledge and being mindful that a particular framing of an issue may not be universally held. This is second-order reflexivity () in which the value systems and theories that shape science, innovation and their governance are themselves scrutinised. Unlike the private, professional self-critique that scientists are used to, responsibility makes reflexivity a public matter ().
Building actors’ and institutions’ reflexivity means rethinking prevailing conceptions about the moral division of labour within science and innovation (). Reflexivity directly challenges assumptions of scientific amorality and agnosticism. Reflexivity asks scientists, in public, to blur the boundary between their role responsibilities and wider, moral responsibilities. It therefore demands openness and leadership within cultures of science and innovation.
Developing a framework for responsible innovation
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Assessment questions 3
title
citation
source
benefit for the taxpayers and appropriate use of public resources in your project
Gov.uk Data ethics framework
Vision
Address the question ‘What does the “ethical” use of data look like for your financial institution? What outcome are we trying to achieve?
Big Data and AI Executive Survey 2021
Do we know how the company uses Big Data and to what extent it is integrated into strategic planning?
Knowing clearly for what purpose the data will be used is important both to make the most of this resource and to identify the critical issues that may arise. Moreover, research has found that public support increases if the context for data use is explained and people are able to deliberate on it.32 As it represents a particularly sensitive area, Ethics Officers should make sure they are aware and up to date with what is happening on Big Data within their organisations.
Business Ethics and Big Data
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Requirements and practices 3
title
citation
source
communicating your purpose
Do people understand your purpose – especially people whom the data is about or who are impacted by its use?
How have you been communicating your purpose? Has this communication been clear?
How are you ensuring more vulnerable individuals or groups understand?
ODI canvas
Strategy
Set strategic goals to achieve this vision, addressing the far-reaching nature of the current challenges and ensuring data ethics are enforceable, embedded in the organisational culture, continuously applied and improved.
Big Data and AI Executive Survey 2021
Compliance should not be seen as a checklist-ticking task
Mere compliance with each of the GPDR and other data protection laws’ provisions is not sufficient for future-proof and technology-responsive privacy policies. Instead, data protection services should be tailored to identify present and future shortcomings in the technological design and application so that data controllers and processors take all the necessary steps to mitigate potential risks for breaching the GDPR. On top of this, ethics accentuates the role of public trust, engagement, transparency and accountability so that data processing meets both the legal requirements and privacy expectations of the public.
Data protection law and ethics
Conduct a risk-benefit analysis
Ensuring proportionality in data processing is a fundamental tenet in data protection law. Not only data processing should be based on an appropriate lawful ground under the GDPR, but it should also be proportionate. This means that the anticipated advantages should outweigh the potential risks for data subjects. Moreover, proportionality requires that any data processing activity is carried out in the least intrusive manner for subjects. These assessments require a consideration and evaluation of societal and ethical parameters.
Data protection law and ethics
Conduct risk-management assessments
A risk-based approach is endemic to the GDPR, because every data processing activity inherently carries risks for the rights and freedoms of data subjects. Technology may raise concerns about the efficiency and applicability of data protection law. This means that compliance with the GDPR requirements and standards is a continuous obligation and that there is no such thing as static and pre-fixed compliance. With regard to the use of Artificial Intelligence, ethical assessments are necessary to ensure the safe application of AI-based technologies, whose consequences are not always predictable.
Data protection law and ethics
Understand the benefits, challenges and limitations of technology
Whereas technology radically transforms society and the available services and products, regulators are often a step behind technologies. Therefore, given the regulatory challenges that new technologies raise, organisations should have the necessary resources to bring insights from industry, academia and the public sector and combine the knowledge and resources across sectors to design both the best data protection and research practices. Indeed, it is necessary that organisations analyse and anticipate gaps in privacy policies and specify the policy and regulatory action to be taken to proactively comply with the GDPR. In this regard, organisations should review the current state of the art and anticipate necessary compliance requirements on an ongoing basis, taking into account the legal, societal and ethical parameters of technology. This will enable organisations to design ethics-grounded data protection operations and align their privacy policies with the GDPR.
Data protection law and ethics
Actively demonstrate ethics
Given the profound connection between a company’s ethical culture and employee engagement, managers and supervisors should work actively to demonstrate a commitment to ethics, foster open communication, promote ethical role modeling, and encourage accountability.
2009 National Business Ethics Survey
The work environment
Higher levels of misconduct and greater perceived pressure to commit a violation equate with lower levels of employee engagement. Therefore, in order to maintain high levels of employee engagement, leaders need not only to set an example but to carefully monitor and manage compliance with corporate ethics standards. All levels of management should be careful not to create work environments where employees perceive that hitting deadlines and meeting revenue goals are the priority regardless of how those goals are achieved.
2009 National Business Ethics Survey
Continuous evaluation
Gov.uk Data ethics framework
Ethical IT strategy
Consider how the implementation of the AI and big data systems ethics guidelines, and other IT-related ethics guidelines, affects the various dimensions of IT management strategy, including overall objectives, quality management, portfolio management, risk management, data management, enterprise architecture management, stakeholder relationship management. Ensure proper adjustment of these processes. There will be different levels of risk involved, depending upon the application, so the levels of risk need to be clearly articulated to allow different responses from the organisation’s ethical protocols.
Guidelines for the Ethical Use of AI and Big Data Systems
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Regulations 3
title
citation
source
Legitimate interests
Article 6(1)(f) gives you a lawful basis for processing where:
“processing is necessary for the purposes of the legitimate interests pursued by the controller or by a third party except where such interests are overridden by the interests or fundamental rights and freedoms of the data subject which require protection of personal data, in particular where the data subject is a child.”
This can be broken down into a three-part test:
GDPR
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Barriers and risks 3
Other aspects 2
title
citation
type
source
Ethical data monetization
detail
KPMG Data and Artificial Intelligence (AI) Ethics Navigator
Data ethics and the law
It is difficult to draw a clear distinction between ethics and law in the current data protection landscape, as the GDPR tends to blur the lines. Complying with the GDPR involves carrying out data protection operations that meet both the GDPR legal and ethical requirements. To this end, ethical assessments should be integrated into the data protection compliance programmes, which should be carried out by experts from various relevant backgrounds and with a holistic understanding of data protection law.
detail
Data protection law and ethics
Strategy and values
Strategy for beneficial and acceptable use of AI in alignment with business strategy and organizational values. Statement of an organization’s value base and the ethical AI principles to guide its AI development and use.
description
Ethical AI Governance Framework
Vestas
The Danish wind energy company Vestas – the largest provider of wind turbines in the world – is a frontrunner when it comes to the use of big data. Before Vestas built its supercomputer with 15 years' worth of data on wind and weather, it could take up to 18 months to erect wind turbines which were optimally positioned in relation to wind and wind production. Today, Vestas uses an algorithm to create a statistical basis for decisions regarding the layout of wind turbines; the work is done with the click of the mouse. Drawing on data from 35,000 public stations which supply measurements on over 150 parameters about every 6 hours, Vestas can produce accurate forecasts for long-term energy production at any point on the globe. Big data is also a part of their ongoing service. Sensors on individual wind turbines along with weather data are used to predict the wear and tear – and plan for the turbine's upkeep.
story
Data Ethics: The new competitive advantage
Ethics and engagement
Positive perceptions of an organization’s ethical culture are associated with higher levels of engagement. Furthermore, management’s commitment to ethics is particularly important for employee engagement
benefit
2009 National Business Ethics Survey
Misconduct
Employees who observed misconduct were less engaged than those who did not. In addition, engaged employees are less likely to feel pressure to commit misconduct
benefit
2009 National Business Ethics Survey
Ethics Risk
Engaged employees are more likely to report misconduct when they witness it, thus reducing the company’s ethics risk.
benefit
2009 National Business Ethics Survey
Future orientated dimensions of responsibility
One alternative to a consequentialist model of responsibility has been to succumb to moral luck (Williams, 1981), to hope that an appeal to unpredictability and an inability to ‘reasonably foresee’ will allow us to escape moral accountability for our actions. Dissatisfaction with both this approach and risk-based regulation has moved attention away from accountability, liability and evidence towards those future-oriented dimensions of responsibility – care and responsiveness – that offer greater potential to accommodate uncertainty and allow reflection on purposes and values
detail
Developing a framework for responsible innovation
Governing emerging tech
Emerging technologies typically fall into what calls an ‘institutional void’. There are few agreed structures or rules that govern them. They are therefore emblematic of the move from old models of governing to more decentralised and open-ended governance, which takes place in new places – markets, networks and partnerships as well as conventional policy and politics ().
detail
Developing a framework for responsible innovation
Responsible innovation
“A transparent, interactive process by which societal actors and innovators become mutually responsive to each other with a view to the (ethical) acceptability, sustainability and societal desirability of the innovation process and its marketable products (in order to allow a proper embedding of scientific and technological advances in our society).”
detail
Developing a framework for responsible innovation
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