Safe and Ethical AI (SEA) Platform Network · Linking Artificial Intelligence Principles (LAIP)

Fourth principle: Bias and Harm Mitigation

Those responsible for AI enabled systems must proactively mitigate the risk of unexpected or unintended biases or harms resulting from these systems, whether through their original rollout, or as they learn, change or are redeployed. AI enabled systems offer significant benefits for Defence. However, the use of AI enabled systems may also cause harms (beyond those already accepted under existing ethical and legal frameworks) to those using them or affected by their deployment. These may range from harms caused by a lack of suitable privacy for personal data, to unintended military harms due to system unpredictability. Such harms may change over time as systems learn and evolve, or as they are deployed beyond their original setting. Of particular concern is the risk of discriminatory outcomes resulting from algorithmic bias or skewed data sets. Defence must ensure that its AI enabled systems do not result in unfair bias or discrimination, in line with the MOD’s ongoing strategies for diversity and inclusion. A principle of bias and harm mitigation requires the assessment and, wherever possible, the mitigation of these biases or harms. This includes addressing bias in algorithmic decision making, carefully curating and managing datasets, setting safeguards and performance thresholds throughout the system lifecycle, managing environmental effects, and applying strict development criteria for new systems, or existing systems being applied to a new context.

Principle: Ethical Principles for AI in Defence, Jun 15, 2022

Published by The Ministry of Defence (MOD), United Kingdom

Related Principles

2. Fairness and Equity

Deployers should have safeguards in place to ensure that algorithmic decisions do not further exacerbate or amplify existing discriminatory or unjust impacts across different demographics and the design, development, and deployment of AI systems should not result in unfair biasness or discrimination. An example of such safeguards would include human interventions and checks on the algorithms and its outputs. Deployers of AI systems should conduct regular testing of such systems to confirm if there is bias and where bias is confirmed, make the necessary adjustments to rectify imbalances to ensure equity. With the rapid developments in the AI space, AI systems are increasingly used to aid decision making. For example, AI systems are currently used to screen resumes in job application processes, predict the credit worthiness of consumers and provide agronomic advice to farmers. If not properly managed, an AI system’s outputs used to make decisions with significant impact on individuals could perpetuate existing discriminatory or unjust impacts to specific demographics. To mitigate discrimination, it is important that the design, development, and deployment of AI systems align with fairness and equity principles. In addition, the datasets used to train the AI systems should be diverse and representative. Appropriate measures should be taken to mitigate potential biases during data collection and pre processing, training, and inference. For example, thetraining and test dataset for an AI system used in the education sector should be adequately representative of the student population by including students of different genders and ethnicities.

Published by ASEAN in ASEAN Guide on AI Governance and Ethics, 2024

4. Human centricity

AI systems should respect human centred values and pursue benefits for human society, including human beings’ well being, nutrition, happiness, etc. It is key to ensure that people benefit from AI design, development, and deployment while being protected from potential harms. AI systems should be used to promote human well being and ensure benefit for all. Especially in instances where AI systems are used to make decisions about humans or aid them, it is imperative that these systems are designed with human benefit in mind and do not take advantage of vulnerable individuals. Human centricity should be incorporated throughout the AI system lifecycle, starting from the design to development and deployment. Actions must be taken to understand the way users interact with the AI system, how it is perceived, and if there are any negative outcomes arising from its outputs. One example of how deployers can do this is to test the AI system with a small group of internal users from varied backgrounds and demographics and incorporate their feedback in the AI system. AI systems should not be used for malicious purposes or to sway or deceive users into making decisions that are not beneficial to them or society. In this regard, developers and deployers (if developing or designing inhouse) should also ensure that dark patterns are avoided. Dark patterns refer to the use of certain design techniques to manipulate users and trick them into making decisions that they would otherwise not have made. An example of a dark pattern is employing the use of default options that do not consider the end user’s interests, such as for data sharing and tracking of the user’s other online activities. As an extension of human centricity as a principle, it is also important to ensure that the adoption of AI systems and their deployment at scale do not unduly disrupt labour and job prospects without proper assessment. Deployers are encouraged to take up impact assessments to ensure a systematic and stakeholder based review and consider how jobs can be redesigned to incorporate use of AI. Personal Data Protection Commission of Singapore’s (PDPC) Guide on Job Redesign in the Age of AI6 provides useful guidance to assist organisations in considering the impact of AI on its employees, and how work tasks can be redesigned to help employees embrace AI and move towards higher value tasks.

Published by ASEAN in ASEAN Guide on AI Governance and Ethics, 2024

V. Diversity, non discrimination and fairness

Data sets used by AI systems (both for training and operation) may suffer from the inclusion of inadvertent historic bias, incompleteness and bad governance models. The continuation of such biases could lead to (in)direct discrimination. Harm can also result from the intentional exploitation of (consumer) biases or by engaging in unfair competition. Moreover, the way in which AI systems are developed (e.g. the way in which the programming code of an algorithm is written) may also suffer from bias. Such concerns should be tackled from the beginning of the system’ development. Establishing diverse design teams and setting up mechanisms ensuring participation, in particular of citizens, in AI development can also help to address these concerns. It is advisable to consult stakeholders who may directly or indirectly be affected by the system throughout its life cycle. AI systems should consider the whole range of human abilities, skills and requirements, and ensure accessibility through a universal design approach to strive to achieve equal access for persons with disabilities.

Published by European Commission in Key requirements for trustworthy AI, Apr 8, 2019

· 2. The Principle of Non maleficence: “Do no Harm”

AI systems should not harm human beings. By design, AI systems should protect the dignity, integrity, liberty, privacy, safety, and security of human beings in society and at work. AI systems should not threaten the democratic process, freedom of expression, freedoms of identify, or the possibility to refuse AI services. At the very least, AI systems should not be designed in a way that enhances existing harms or creates new harms for individuals. Harms can be physical, psychological, financial or social. AI specific harms may stem from the treatment of data on individuals (i.e. how it is collected, stored, used, etc.). To avoid harm, data collected and used for training of AI algorithms must be done in a way that avoids discrimination, manipulation, or negative profiling. Of equal importance, AI systems should be developed and implemented in a way that protects societies from ideological polarization and algorithmic determinism. Vulnerable demographics (e.g. children, minorities, disabled persons, elderly persons, or immigrants) should receive greater attention to the prevention of harm, given their unique status in society. Inclusion and diversity are key ingredients for the prevention of harm to ensure suitability of these systems across cultures, genders, ages, life choices, etc. Therefore not only should AI be designed with the impact on various vulnerable demographics in mind but the above mentioned demographics should have a place in the design process (rather through testing, validating, or other). Avoiding harm may also be viewed in terms of harm to the environment and animals, thus the development of environmentally friendly AI may be considered part of the principle of avoiding harm. The Earth’s resources can be valued in and of themselves or as a resource for humans to consume. In either case it is necessary to ensure that the research, development, and use of AI are done with an eye towards environmental awareness.

Published by The European Commission’s High-Level Expert Group on Artificial Intelligence in Draft Ethics Guidelines for Trustworthy AI, Dec 18, 2018

· 8. Robustness

Trustworthy AI requires that algorithms are secure, reliable as well as robust enough to deal with errors or inconsistencies during the design, development, execution, deployment and use phase of the AI system, and to adequately cope with erroneous outcomes. Reliability & Reproducibility. Trustworthiness requires that the accuracy of results can be confirmed and reproduced by independent evaluation. However, the complexity, non determinism and opacity of many AI systems, together with sensitivity to training model building conditions, can make it difficult to reproduce results. Currently there is an increased awareness within the AI research community that reproducibility is a critical requirement in the field. Reproducibility is essential to guarantee that results are consistent across different situations, computational frameworks and input data. The lack of reproducibility can lead to unintended discrimination in AI decisions. Accuracy. Accuracy pertains to an AI’s confidence and ability to correctly classify information into the correct categories, or its ability to make correct predictions, recommendations, or decisions based on data or models. An explicit and well formed development and evaluation process can support, mitigate and correct unintended risks. Resilience to Attack. AI systems, like all software systems, can include vulnerabilities that can allow them to be exploited by adversaries. Hacking is an important case of intentional harm, by which the system will purposefully follow a different course of action than its original purpose. If an AI system is attacked, the data as well as system behaviour can be changed, leading the system to make different decisions, or causing the system to shut down altogether. Systems and or data can also become corrupted, by malicious intention or by exposure to unexpected situations. Poor governance, by which it becomes possible to intentionally or unintentionally tamper with the data, or grant access to the algorithms to unauthorised entities, can also result in discrimination, erroneous decisions, or even physical harm. Fall back plan. A secure AI has safeguards that enable a fall back plan in case of problems with the AI system. In some cases this can mean that the AI system switches from statistical to rule based procedure, in other cases it means that the system asks for a human operator before continuing the action.

Published by The European Commission’s High-Level Expert Group on Artificial Intelligence in Draft Ethics Guidelines for Trustworthy AI, Dec 18, 2018