This awareness is critical for responsible machine learning and artificial intelligence for two key reasons. First, when AI and ML development is understood as an endeavour which exists within the domain of human culture and decision-making, any claim for design neutrality dissolves. Second, when we know who is onboard in the development of a project, we can ascertain a better understanding of a project’s capabilities.

For example, Gallaudet University (a university that operates entirely in American Sign Language) enlists d/Deaf architects for the design of “DeafSpace” or sign-language friendly architecture, altering otherwise unknown architectural challenges relating to space, proximity, wall colour, and sensory reach (Gallaudet, 2023). In this case Deafness, fluency in ASL and a degree in architecture serves as a specific lived context that informs how physical space can be designed for sign language communication specifically. This example illustrates the way that positionality has a direct relationship to design and design claims, thus making specific the ways that different applied knowledge is produced.

Humanizing machine learning processes is intimately linked to intersectionality as it acknowledges that each person develops partial perspectives and expertise based on their own lived experience. Being reflexive is recognizing our own, and working with individuals who complement them. Reflexivity leads to more transparency, because a reflexive approach entails explicitly aligning a project’s objectives with the abilities and life experiences of the project’s participants.

Machine learning models are designed and developed by people, and datasets are never ‘raw’. It is important to recognize this to challenge the idea of disembodied data subjects which works to obscure their varied interests, lived experiences, and locations within power relations (Gray & Witt, 2021). Moreover, human decision-making goes into the collection, organization, and preparation of data, and this labor is often unrecognized and undervalued. It is well documented that ML development relies on extractive forms of labor in the organization and labeling of data. Addressing this level of inequity in your project means having an awareness of how your data has been organized, where it was trained, and by which people. In addition to interrogating gender equity at the design stages of AI/ML development, equity also requires noticing the areas of AI/ML development that have been historically precarious, underpaid, invisibilized, and where workers from the Global South are often over-represented (Crawford, 2021; Irani, 2015).

Additionally, shedding light on the materiality of ML systems also means recognizing the environmental implications of technology development. The extraction of natural resources and the large carbon footprints linked to the lifecycles of machine learning are important to consider and be acknowledged in transparent ways: Only then can strategies be put in place to minimize these impacts in the future. Recognizing the reliance on extractive labour and the environment and naming these issues is an important step to taking measures to alleviate negative material effects and work to change unequal systems.

Biases in the system can be identified and mitigated at various stages, from data collection to model deployment. ML/AI practitioners have highlighted several types of biases that can occur in the process of developing AI systems: historical bias, representation bias, measurement bias, aggregation bias, learning bias, evaluation bias and deployment bias (Suresh & Guttag, 2021, 3-4). Historical bias refers to biases in datasets that already exist in the world, and hence get reproduced in machine-learning systems; text datasets, for instance, which are used to develop large language models, are known to contain gendered biases and stereotypes. Representation bias refers to situations where a data sample used to develop a model doesn’t represent the use population and underrepresents parts of it. Evaluation bias, on the other hand, occurs when benchmarks employed to evaluate ML models and algorithms misrepresent the use population. For instance, facial recognition technologies, have well-known representation and evaluation biases. Higher error rates for dark-skin tones are well-documented, and this has been linked to the underrepresentation of dark-skinned faces in the training datasets used to develop their models.

These are just a few examples of biases that can arise from AI systems. To address all these biases, machine learning algorithms and models should be monitored and evaluated regularly using algorithmic fairness measures and techniques, in addition to human auditing and intervention.

As a first step towards transparency, accountability mechanisms should be established in collaboration with relevant communities either before or in the early stages of the project. This means establishing a meaningful channel of communication and outreach among the ML/AI team and relevant communities and stakeholders. As the project develops, open access to the datasets, algorithms, as well as development and decision-making processes should be available to these same communities. This openness has the potential to facilitate a framework of trust and reliability if done reflexively, mindfully and respectfully. ML/AI practitioners can work together with communication teams to document and communicate the risks and potential improvements that can be made at various stages of the development process to relevant communities and stakeholders. This addresses concerns of ‘black boxing’ and encourages a dialogue among practitioners, the wider project team and relevant community members.

Accountability implies that humans, at all stages of a project, are ethically responsible in their positions for any consequences that might arise from an ML system. It starts with thoroughly educating researchers and designers about the social impacts of their work. This can be done in workplaces, through training and the implementation of guidelines and resources for development teams. In a larger context, this also gets achieved through a more interdisciplinary training in STEM higher education programs. Finally, having processes such as presenting clear avenues of redress to the public and designating accountable individuals in case of negative social impacts can constitute meaningful solutions to hold ML systems’ creators accountable.

Rather than addressing a generalized audience, the dissemination of knowledge should address the specific context of the community in which machine learning/AI is situated or has an impact on. Access, then, depends on the needs of the community at hand. Accessible knowledge dissemination thus can take various forms: Access provisions responding disability, language(s) spoken, expertise, internet service, social or cultural understanding, and/or lack of information.