As data practitioners with concern for humanity we have been excited by the responsible AI conversations that have popped up over the last few years. We have also been increasingly frustrated by the lack of consolidated practical development tools to ensure that we are doing the best that we can. So at Lelapa AI we have put one together. It aims to ensure that no form of analysis should be unleashed upon the world without clear communication of the mechanisms used to arrive at its conclusions. Nor without clear communication of the intended usage context.
The framework we have developed is a self check for data practitioners in industry (though it is likely to be useful for those in academia too). It is designed to prompt the right questions in order to highlight potential problematics. It also aids in the placement of protective measures required for responsible development and the management of unintended outcomes.
The Framework in Context
As data scientists not all the projects we work on include data sourcing or even modelling. Sometimes we find ourselves in positions where we are part dev and are requested to deploy models as a service. Sometimes we are responsible for the process end to end. Sometimes we are responsible for small pieces of the bigger picture. Either way, it leads to many different interconnected debug points where things could go wrong.
This framework brings together many pre-existing tools that act as independent parts of a flexible structure. The areas included in this framework include but are not limited to ethics, security, privacy, responsibility, redress, transparency, fairness, explainability and accountability. These areas are considered throughout the entire lifecycle of a project from conceptualisation, through development, deployment and monitoring.
Movements Toward AI Regulation
We still have a long way to go in terms of legally protecting the planet and its people from AI for Bad. It will be an agile process of keeping up with advancements while carefully selecting ways in which advancements can contribute to the better good. But the drive to put in place formal regulations for AI has begun.
Use of facial recognition software by law enforcement has been banned in a number of states in the US and in Europe as well, and tech giants are in on it too. Measures have been put into place to protect personal information with GDPR in Europe implemented in 2018. South Africa followed with the POPI Act. Though personal data is protected, regulation for predictive models is virtually non-existent.
The European Commission released a Proposal for a Regulation — the Artificial Intelligence Act — in April of 2021. The aim of the proposal is to encourage responsible uptake of AI technologies.
EU AI Regulation Proposal
The regulation proposed applies to all machine learning techniques including supervised, unsupervised and reinforcement learning. It also includes logic and knowledge-based systems and statistical approaches such as Bayesian estimation and optimisation methods.
The proposal refers mainly to high-risk applications which include Biometric Identification, Management of critical infrastructure, Education, Employment, Essential services access, Law enforcement, Migration management and Administration of justice.
Framework Structure
The framework proposed here follows the declaration of conformity format. But the idea is that this framework is followed for ALL data-related decision making processes and not just those that are considered high risk.
In the same way we weave value systems into people so that they behave in ways that support the ethical fabric of society. We believe it is imperative to weave values into the practice of developing decision enablement mechanisms.
There are three main areas concerned with regulating AI:
Machine Conceptualisation
This phase is primarily concerned with the ethics of a particular use case. We want to ensure that the intentions of the use case are pure. Even in the case that the intentions of a use case are pure, the metrics used to determine the use case's success may be ill suited resulting in unethical models.
Machine Development
This phase deals primarily with algorithmic fairness and addressing algorithmic bias which is largely a problem due to the black box nature of machine learning models. This is addressed by a process we have dubbed as EMA (Exploratory Model Analyses).
Machine Management
This phase primarily deals with the assurance that the model system developed is replicable, robust and secure and that the relevant measures are put into place to ensure accountability of the systems and its creators. Also very important in this phase are considerations around visualisation and interpretation of results.
Intended Use
In the case of any of the project categories below wherein the project results in an insight that informs any kind of decision making process, the relevant sections should be completed:
- Machine learning (supervised, unsupervised, semi-supervised, reinforcement learning)
- Logic and knowledge-based analysis and calculations
- Statistical projects e.g. Bayesian Estimation, optimisation methods and other summary statistics
This framework does not, by any means, encompass everything and will require continuous updates along with cultural, societal and technological change. It will, however, hopefully get your brain thinking about things it may not have considered before.