Tag: Ethical Implications of machine Learning and Artificial Intelligence

Artificial Intelligence design guiding principles: Review of “Recommendation of the council on Artificial Intelligence”

(Image Taken from Pixabay)

The document ” Recommendations of the Council on Artificial Intelligence” was authored by the Organization for Economic Cooperation and Development (OECD), and was published in France in May 2019. It aims to foster innovation and trust in AI by promoting the responsible stewardship of trustworthy AI while ensuring respect for human rights and democratic values; whose adherence would be ratified by the organization’s member countries, and some non-members such as: Argentina, Brazil, Colombia, Costa Rica, Malta, Peru, Romania, and Ukraine.

Being joint authored, by members of a body that groups the efforts of the governments of several countries in the Americas, Europe, the Middle East, and Australia: I have classified the author type as “Intergovernmental Organization”. Also, in the light of the objective pursued by the recommendations and the type of principles proposed, I have classified the document as “Policies Principles”. Both classifications will allow me to make future contrasts between documents and authors of the same type; enriching the analysis that I aim to present in this series of posts.

The Recommendation identifies five complementary values-based principles for the responsible

stewardship of trustworthy AI and calls on AI actors to promote and implement them:

  1. Inclusive Growth, Sustainable Development, and Well-being: proactively engage in responsible management of trustworthy artificial intelligence searching for benefits for people and the planet, increase human capacities and improve creativity, advance toward the inclusion of underrepresented populations by reducing economic, social, gender, and other inequalities; and in the protection of the natural environments,
  2. Human-Centred Values, and Fairness: respect the rule of law, human rights and democratic values during all the life cycle of the artificial intelligence solution –  freedom, dignity and autonomy, privacy and data protection, non-discrimination and equality, diversity, equity, social justice and work -; implementing mechanisms and safeguards, such as a human capacity for self-determination, that are appropriate to the context and consistent with the state of art,
  3. Transparency and Explainability: provide relevant information, appropriate to context and consistent with state of the art: (a) to  promote a general understanding of the operation of  AI systems,  (b) to enable stakeholders to be aware of their interactions with AI systems,  (c) to allow those affected by an artificial intelligence system to understand the outcome, and  (d) to allow those adversely affected by an AI system to challenge their outcome based on easy-to-understand information about the factors and logic that served as the basis for the prediction,  or  recommendation,
  4. Robustness, Security, and Safety: develop robust and  safe AI systems, and protect them  throughout their life cycle so that  –  under normal use, foreseeable use or misuse, or other adverse conditions  – the keep functioning properly without becoming a security risks; ensuring  traceability  towards  data sets, processes and decisions made, and  applying a systemic approach to risk management at every stage of the AI system lifecycle that includes factors such as:  privacy, digital security, security and bias; and,
  5. Accountability: make the AI actors accountable for the proper functioning of AI systems and its correspondence with the proposed principles, according to their roles, the context and consistent state of the art.

Additionally, a set of recommendations are made, as can be seen below, for the definition of national policies and international cooperation between the adherent countries in favors of trustworthy Artificial Intelligence:

  1. Investing in AI research and development,
  2. Fostering a digital ecosystem for AI,
  3. Shaping an enabling policy environment for AI,
  4. Building human capacity and preparing for labor market transformation, and
  5. International co-operation for trustworthy AI.

On this occasion will limit myself to only comment on the principles. Since the recommendations are aimed at public policymakers; and that is a context which I do not have enough experience in.

From my computer science background with hands-on experience in software project management, I find it difficult to adopt these principles as a methodological reference without them being subject to additional layers of interpretation, and integration into tools such as standards or checklists, to name a few examples. As I have already mentioned in other posts; on the one hand, standards would support the assurance of expected outcomes of the artificial intelligence solutions since early development stages in accordance with the framework scope delimited by the proposed principles; and, on the other hand, checklists are an effective tool in the verification stages, used to whereas the designed solution complied with the proposed principles – using the same examples -.

In that same line of thoughts, from my experience defining checklists, and as a member of international software development standards designing working groups, I can highlight the following elements:

  • The definition of the conceptual neighborhood for variables related with concepts such as: discrimination, bias, justice, and equity, in the context of artificial intelligence; that can serve as a frame of reference for the software developer at every stage – including maintenance – of the development process,
  • The operationalization of the concept of well-being as a dependent variable on the discriminatory or non-discriminatory nature of decisions based on decisions, predictions, and/or recommendations proposed by AI systems,
  • The operationalization of the concept of “natural environment friendly” as a dependent variable on the aggressive or non-aggressive nature of made decisions based on decisions, predictions, and/or recommendations proposed by AI systems,
  • The formalization of metrics aimed at evaluating how discriminatory or aggressive with the natural environment is a decision, prediction, and/or recommendation proposed by AI systems,
  • The definition of checklists to guide the developer of AI systems during the verification and measurement of these variables at every stage of the development process,
  • The demarcation of which values resulting from the measurements and what factors within the checklists triggers a formal review of the architecture baseline of the current version of the AI system being developed,
  • The demarcation of which values resulting from the measurements and of which factors within the checklists triggers a formal change request in the case of medium and large projects, with medium and high complexity,
  • The creation of a competent authority that continuously assesses the adequacy of the formalization of measurements to the corresponding social context, including that the causal elements of discrimination and other related terms are variable over time,
  • The operationalization of the variables on which human rights and the democratic values are based on which artificial intelligence solutions are expected to be in correspondence with,
  • The operationalization of “transparency” and “understanding” as dependent variables within the understanding of the methods used for data processing, which can be used in defining a metric that assesses the levels at which the understanding of the methods and the results of the AI system by potential stakeholders and auditors, can be expressed with, and
  • The definition of information management flows associated with the use of AI systems including the necessary elements (access policy to which piece of information, period of time the information will be available, for example) and moderating the communication between the stakeholder and the decision maker (regardless of the latter) to be incorporated – ex officio – in Report modules; helping those adversely affected by an AI system supported decision, to obtain relevant information and details of the decision.

As necessary intermediate layers towards the principle’s adoption as a methodological reference for the design of artificial intelligence solutions.

After an analysis of the language used in the document, in which I used the NLTK library and Python´s development environment for extracting the 50 most frequent n-grams from the charter´s body text it turned out that:

  • The uni-grams with relative frequencies greater than .50 units described the objective intended with the principles and recommendation proposal, or the variables in which they are expressed: policy (1.10), international (1.02), legal/ trustworthy/ development (.98), council/ principles/ work (.78), digital (.69), human (.65), operations/ stakeholders/ systems (.61), and responsible/ implementation/ systems (.57); in contrast with: stewardship/ rights/ inclusive/ sustainable/ recommendations (.33) that also being elements among the objectives pursued by the document are less represented along the text body.
  • The bi-grams, from their part, delimited the document´s field of action in the context described by the unigrams, exhibiting with higher relative frequencies the terms: trustworthy ai (.90), ai actors (.53), legal instruments/ international co-operation (.45), and responsible stewardship/ stewardship trustworthy (.33). Although, other variables such as risk management/ growth sustainable/ security safety/ digital ecosystem/ privacy data/ and ai government exhibit minimum relative frequency values with 0.16 units each.
  • The tri-grams, similarly, exhibit a greater representation of the terms linked to the macro-objective of the document with: international co-operation instruments (.45), responsible stewardship trustworthy/ ai systems lifecycle (.33); while the pursued objectives are less represented: human centred values/centred values fairness/ robustness security safety/ investing ai research/ fostering digital ecosystem/ building human capacity, preparing labor market/ practical guidance recommendations (.12) and, artificial intelligence first/ first intergovernmental standard (.08).

I would like to conclude by saying that the recommendations of the council on AI addressed in this post, along with other documents I am including in this series; constitute an effort to solve some of the ethical problems rooted in the design and use of artificial intelligence solutions. In this case, specifically in the context of public policy; the remaining documents will include other scenarios. Also, I would like to add that, with this reading exercise I seek to draw attention to the opportunity of public policy designers and designers of artificial intelligence solutions to collaborate in the achievement of a common goal: what is the responsible design of artificial intelligence.

If you are interested about this topic and have any idea that complements this review of the Recommendations of the Council on Artificial Intelligence let me know with a comment.

Right from Wrong

Right and Wrong
(Image taken from pixabay)

I often read or listen to people´s ideas on handling accountability in the artificial intelligence field of study and solution development. I could spare the main trends in the two: the first being the need for formal modeling of human morality and ethics, and its further coding into algorithms; and the second, the need to build a machine learning solution who follows (the idea of how is still a black box) human moral and ethics rules. Both, in my opinion, are opposite poles just as the sciences and backgrounds of their promoters. A practical example of the eternal dichotomy between hard-sciences and soft-sciences practitioners and academics.

One can find several instruments supporting the latter trend like the “Statement on Artificial Intelligence, Robotics and ‘Autonomous’ Systems” of the European Group on Ethics in Science and New Technologies; the “The Toronto Declaration: Protecting the rights to equality and non-discrimination in machine learning systems”; or the multinational research project aiming to arise with a number of universal moral rules showcasing a common understanding of every society on the planet. All of them with strong fundamentals but lacking on technical specifications to be followed by computer scientists and software developers.

On the other hand, the first trend finds some mechanisms to evaluate responsibility degree, or to accountability recognition after bad decisions are made. Something similar to David Gunkel´s “responsibility gap” theory. One example of this is the calibration checks algorithms; which will tell whereas an algorithm is being biased at a given point comparing the results for a target object in different datasets (the object included). However, yet exist a long path before we are able to formalize ethical and moral frameworks for artificial intelligence solutions to work with on their learning process.

What brings me to request the attention upon artificial intelligence´s dependence on machine learning development as per the decision making processes, consequently the learning core its a capstone; then machine learning methods and techniques aim to simulate human behavioral systems on its procedures. What makes the very object we are criticizing the reflection of our own – let´s not simplify it by saying poor – but complex learning and decision-making processes.

What could be an accurate answer to how effectively we teach right from wrong to children or other individuals? We can all agree prisons are full of people who were found accountable after a bad decision was made. I like this particular statement cause also includes the number of individuals that have been erroneously convicted when judged.  To verify that one only need to consult the innocence project records in the United States of America just to put an example. Where, as a result of bad decisions, some individuals have spent a mean time of 32 years in prison before their innocence was proved. An issue that is currently costing millions to the American government to amend.

Yet a large number of critics believe AI Systems has to be impeccable when learning from our biases (present in datasets), misconceptions (introduced by supervised learning to mention one example), and practices (limiting the machine capability to our human system context due we are usually afraid of what we cannot controlled/explain). Therefore, our way of teaching, learning, experiencing the way of distinguishing right from wrong as humans does not have to be the same for AI systems. Paralelly AI Systems are not the ones should be accountable for when a bad decision is made. We will be incurring in responsibility transference otherwise. Our perceptions and laws need to catch up with AI development.

An approach to tackle this problem could be to ideate how to work the data AI solutions will further use to learn and train a topic left aside most of the time. As in regard to data, the frequently tackled issues are related to duplicity, noise, absence, normalization, access, privacy, and governance; not bias. Complementarily, we also need to develop new methods tunned with the conception of AI solutions being machines and not human imitations; acknowledging we: developers, consumers… have the entire responsibility upon any outcome produced by an AI solution – except when such AI solution was designed and created by other AI solution 🙂 -. Otherwise, ideals such as fairness or justice will become even more subjective issues in a data-driven society.

Distinguishing right from wrong, we are the ones called to do that.

How to reference: Varona, Daniel. 2019. Right from wrong. www.danielvarona.ca [Year of Creation:2019] http://www.danielvarona.ca/2019/06/07/right-from-wrong/ [Consulted on: DDMMYY].