Practical Challenges in Explicit Ethical Machine Reasoning

01/04/2018 ∙ by Louise Dennis, et al. ∙ University of Liverpool 0

We examine implemented systems for ethical machine reasoning with a view to identifying the practical challenges (as opposed to philosophical challenges) posed by the area. We identify a need for complex ethical machine reasoning not only to be multi-objective, proactive, and scrutable but that it must draw on heterogeneous evidential reasoning. We also argue that, in many cases, it needs to operate in real time and be verifiable. We propose a general architecture involving a declarative ethical arbiter which draws upon multiple evidential reasoners each responsible for a particular ethical feature of the system's environment. We claim that this architecture enables some separation of concerns among the practical challenges that ethical machine reasoning poses.

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1 Introduction

There has been an explosion of interest in Ethics and Artificial Intelligence as evidenced by several high profile initiatives considering the issue such as the

IEEE Global Initiative on Ethics in Artificial Intelligence and Autonomous Systems and the BSI Standard 8611: Guide to the Ethical Design and Application of Robots and Robotic Systems. While these initiatives generally take a wide-ranging view of the subject considering everything from the deployment of autonomous weapons, the societal impact from the potential loss of jobs, to the privacy issues that result from big data and social media they also consider, as a topic, the implementation of ethical reasoning in machines, often referred to as machine ethics but which we will here refer to as ethical machine reasoning in order to highlight our consideration of computational reasoning about ethical issues.

One of the key challenges facing the implementation of ethical machine reasoning is that no consensus exists on the nature of morality, the key moral values, how morals relate to ethical rules and how competing ethical rules can be decided between in specific contexts. We will refer to these issues as philosophical challenges facing the implementation of ethical machine reasoning.

In this paper we contend that there are a range of other challenges faced by ethical machine reasoning which would make it a challenging area of artificial intelligence even if the philosophical challenges were resolved. These practical challenges

relate to questions of how ethical reasoning is to be implemented. They let us identify the implementation of ethical machine reasoning as a distinct sub-field of automated reasoning in general and demonstrate that it is not possible to satisfactorily implement ethical machine reasoning simply by taking pre-existing automated reasoning techniques and applying them to the ethical theory of your choice.

In this paper we seek to understand the practical challenges that characterise machine ethics. We frame this understanding around the discussion of existing systems that claim to implement ethical reasoning and propose a general software architecture for ethical machine reasoning which would support a variety of solutions to these challenges.

2 Survey of Ethical Machine Reasoning Implementations

All machine reasoning systems can be viewed a ethical reasoning systems from some level of abstraction, so we here restrict ourselves to systems that are explicitly ethical in the sense of Moor [Moor2006] (i.e., they reason explicitly about ethical concepts). There are few examples of such systems and we view the main ones here – our purpose in doing so is to highlight the practical issues faced by the implementation of ethical machine reasoning.

2.1 Ethical Governors

The first implementation of ethical machine reasoning is generally credited to Arkin et. al, of the Georgia Tech Mobile Robot Lab [Arkin, Ulam, and Duncan2009, Arkin, Ulam, and Wagner2012] who outline the architecture for an ethical governor for automated targeting systems for autonomous weapons. This governor was charged with ensuring that any use of lethal force was governed by the “Law of War”, the “Rules of Engagement” and was proportional.

The governor was implemented as a separate module that intercepted signals from the underlying deliberative system and, where these signals involved lethality, would go through a process of evidential reasoning

which amassed information about the situation in a logical form and would then reason about the evidence using constraints represented as prohibitions and obligations. If any prohibitions were violated or obligations unfulfilled then the proposed action would be vetoed. If no prohibition were violated then the governor would proceed to a “collateral damage” estimation phase and attempt to find a combination of weapon system, targeting pattern and release position that would maximise the likelihood of neutralising the target while minimising collateral damage.

The authors note that “it is a major assumption of this research that accurate target discrimination with associated uncertainty measures can be achieved despite the fog of war” and the case studies they perform using their implementation are based on this assumption and provide appropriate information up front as part of the scenario.

This initial work on ethical governors was then re-implemented in a new setting of healthcare [Shim and Arkin2017]

. In this setting the ethical governor monitors not an underlying autonomous system but the interactions between a patient with Parkinson’s Disease and a caregiver. The reasoning behind such a monitoring system is that sufferers from Parkinson’s Disease frequently lose control of their facial musculature, this means that many non-verbal cues and interactions between patient and care-giver are lost which can result in stigmatisation between a caregiver and patient and a decrease in the quality of patient care. This ethical governor combines an evidential reasoner with a rule-based system (as opposed to a constraint reasoning system) but the basic architecture is the same. The evidential reasoner produces an assessment of the environment based on cues such as raised voices which are represented in a logical form. The rule-based system then reasons about this logical information in order to select appropriate intervening actions such as verbal interventions or indicative gestures.

2.2 GenEth

The GenEth system [Anderson and Anderson2014]

is designed as an ethical dilemma analyzer. Its purpose is twofold. Firstly, it demonstrates how input from professional ethicists can be used via a process of machine learning to create a

principle of ethical action preference111We note that the terminology of ethical principles is used widely but inconsistently throughout the literature. which can be applied to situations in order to determine appropriate action. GenEth analyses a given situation in order to determine its ethical features (e.g., that harm may befall a patient in some healthcare scenario), these features then give rise to duties (to minimize or maximize that feature). The principle of ethical action preference is used to compare two options: each option is assigned a score for each relevant ethical feature, the difference between these two scores is then used by the principle which partitions the -dimensional space defined by the feature comparisons (one dimension for each feature) into regions using inequalities. Each partition of the space specifies which of the compared actions is to be preferred in that region so, for instance if the first action is significantly worse in terms of privacy than the second (e.g., the difference in their score on the privacy feature is greater than 2) but the second action is a little worse in terms of patient safety (e.g., the difference in their score is less than -1) then the partition might specify that the first action is to be preferred.

Initially GenEth was implemented as a standalone system which was used to capture information from medical ethicists on decisions that should be made in particular, manually generated, scenarios. It has subsequently been connected as a decision-making component on top of a simulator for Nao robots [Anderson, Anderson, and Berenz2016] and evaluated in scenarios where the robot must choose between six possible actions (such as charging itself, reminding a patient to take medication, and notifying an overseer of problems). These actions are evaluated on an ongoing basis using a principle which considers eight ethical features (honour commitments, maintain readiness, minimise harm, maximise good, minimise non-interaction, respect autonomy and maximise the prevention of immobility).

GenEth is able to give explanations for its decisions in terms of its partition of the space – so it can state how two options compared on the various ethical features in its judgement and refer to the statement of the principle to then justify the subsequent choice.

2.3 Ethical Consequence Engines

Winfield et. al, of the Bristol Robotics Lab [Winfield, Blum, and Liu2014, Vanderelst and Winfield2016] have investigated systems based on the concept of an Ethical Consequence Engine. This consequence engine uses simulation to evaluate the impact of actions on the environment. In particular it simulates not just the actions of the robot itself but also simulates the activity of other agents in the environment, based on some simplifying assumptions about movement and intended destinations of humans and other robots. This allows the robot to determine not only if its actions have directly negative consequences (e.g., colliding with a person) but if they have indirectly negative consequences (e.g., failing to intercept a person who might otherwise come into danger).

There are two versions of the ethical consequence engine system the first of which [Winfield, Blum, and Liu2014] was implemented on e-Puck robots and evaluated all possible actions in the environment based on a discritization of the space of operation, while the second [Vanderelst and Winfield2016] was implemented on Nao robots and used a sampling procedure to evaluate a specific sub-set of options. Each option is scored using various metrics such as the closeness of any humans to “danger”, the closeness of the robot to “danger” and the closeness of the robot to its goal. These metrics are then combined in a weighted sum and the highest scoring option chosen. The first of these systems was also verified [Dennis, Fisher, and Winfield2015] in a process that involved converting the metric based evaluation of the robot actions into logic based reasoning over outcomes that compared the severity of the outcome and who suffered the concequences (human or robot).

2.4 Ethan

The Ethan system [Dennis et al.2016a] was developed to investigate ethical decision making in exceptional circumstances with a particular emphasis on verifiability. In the Ethan system a rational agent, based on the Beliefs-Desires-Intentions model of agency [Rao and Georgeff1995] was used to reason about the ethical risks of plans proposed by an underlying planning system. In this system the operation of reasoning in normal circumstances was assumed to be ethical by default (i.e., there was an assumption that appropriate ethical properties were guaranteed by a process of testing or verification of the decision-making process) but that in exceptional circumstances the system might need to make use of Artificial Intelligence techniques such as planning or learning which are inherently challenging to verification. [Dennis et al.2016a] considers the case of a planning system that returns candidate plans to the agent which are annotated with any ethical principles222These can be considered broadly equivalent to GenEth’s ethical features. impacted by the plan. The case study looked at scenarios involving unmanned aircraft and plans were annotated with the nature of any collisions that might take place or violations of the Rules of the Air. Ethan then reasoned using a context specific ethical policy which imposed an ordering on plans based upon the ethical principles they violated.

Model Checking [Clarke, Grumberg, and Peled1999] was then applied to the Ethan agent in order to verify a number of properties, including that the agent was programmed to correctly obey the specified ethical policy – i.e., that if it selected a plan that violated some ethical principle then this was only because all the other available options were worse.

3 Observed Features

We can observe a number of features both individually and jointly across these systems that begin to define the space of practical challenges that face systems seeking to implement explicit ethical reasoning.

3.1 Multi-Objective

While terminology across these systems is inconsistent we note that all of them operate on the assumption that the situation in which the system finds itself may have a number of potential ethical impacts – whether these are referred to as ethical features, ethical principles, ethical constraints or by some other language. We will refer to these as ethical features for convenience.

While we anticipate that in most everyday reasoning situations at most one ethical feature is at stake – i.e., in many cases none of the available options have particular ethical features (answering the front door, for instance) – in nearly all cases the point of the ethical reasoning is to limit goal-directed behaviour according to ethical considerations (though some of the systems treat goal-directed behaviour as an ethical feature expressed, for instance, as obedience to the human). However, consideration of ethical features rapidly leads to the conclusion that there will be situations where the system must somehow choose between them, as well as limiting goal-directed behaviour. So GenEth has its partitioning of the space that compares individual options according to their ethical features, Ethan has its context-dependent ethical policy while the Ethical Consequence Engine prioritises humans over robots, and within that the extent of the harm that may befall the agent.

We note that this makes ethical reasoning inherently multi-objective which is a practical challenge to many techniques for controlling decision-making in machines. In particular this kind of reasoning is challenging for techniques that seek to maximize or minimize some value for while at a very abstract level we can say the point of ethical reasoning is to maximise human well-being (as suggested by [Dignum et al.2017]) this is not a concept easily captured in a function. Instead abstract concepts such as well-being are concretized as ethical features – ethical machine reasoning then becomes about deciding what balance among these features is most likely to improve or preserve human well-being (or some equivalent abstract general value)333

We probably need to accept that in the absence of an agreed philosophical framework for morality, the best any feature-based reasoning can hope to achieve is choosing the right outcome most of the time.

.

It is tempting to drop down to a lower level and utilise multi-objective optimisation [Miettinen1998], but this works against many other features we require such as scrutability and verifiability.

3.2 Heterogenous Evidential Reasoning

The Ethical Governor systems are the only approaches that explicitly describe their architecture as consisting of first an evidential reasoner which translates sensory information into a logical form and then a second reasoner that makes a decision based upon the logical translation. However both GenEth and Ethan also take something approaching this form, assuming that information has been expressed in some logical or equational form for use by the system. Interestingly the case studies presented for all these systems either adopt very simple evidential reasoning mechanisms, or use some oracle to provide information in an appropriate form and focus on the subsequent reasoning.

While the ethical consequence engines do not use a logical expression of data, they too crucially involve an evidential reasoning phase by using a simulator to make predictions about the outcomes of actions. It is also clear, particularly in [Winfield, Blum, and Liu2014] that although the simulation results are converted to metrics and then employed in a utility function it is envisaged that this captures logical-style reasoning about the severity of outcomes and the relative importance of humans and robots.

The ethical consequence engines use simulation to make predications about safety outcomes, but it is easy to see that simulation is not effective in, for instance, establishing risks to human free will and autonomy and while simulations of information flows might be sufficient to determine privacy risks in social media settings, it is unlikely to be sufficient when considering information flow around smaller groups of people such as families and health workers. An ethical machine reasoning system operating on a complex set of ethical features will need to use a variety of heterogeneous mechanisms to perform evidential reasoning about the situation it finds itself in.

The nature of this heterogeneous evidential reasoning appears to be a particularly under-explored aspect of ethical machine reasoning, even given the relative youth of the field and the small number of implemented systems. It is also of note that the ethical features considered by the ethical systems we survey vary wildly, sometimes within a given system – for instance the GenEth case study [Anderson, Anderson, and Berenz2016] considers both “readiness” (which relates primarily to how much charge the robot has) and the far more abstract concept of “good” as ethical features. Ethan treats “Do not collide with people” and “Do not collide with aircraft” as distinct ethical features despite the fact that both are clearly related to safety. Understanding of what makes a suitable ethical feature, as an atomic concept for ethical machine reasoning and whether there is some heirarchy among these (e.g., safety is associated with features specifying whose safety), and the extent to which they need to be annotated with, for instance the degree of severity of the impact and the uncertainty about the outcome, is lacking and is a challenge that clearly has both practical and philosophical aspects.

3.3 Real Time

Most of these systems have had to make some compromise with the real time aspects of ethical reasoning in machines. We note that this is not always the case. The ethical governor system that mediates between patient and care-giver has more time available for reasoning than does the ethical consequence engine must react quickly to prevent an accident and in general we can envisage advisory systems for committees of people that would have minutes rather than fractions of a second for deliberation.

However, as a general observation, ethical machine reasoning must often perform complex reasoning in real time.

3.4 Proactivity

Most of the implementations we have surveyed are reactive – i.e., their purpose is to veto or order plans/actions suggested by the underlying system. However several of them acknowledge a need for proactivity – the ability not only to veto plans but to suggest separate courses of action. This is most obvious in the Ethical Consequence Engine in which the whole point of the experiment is to divert the robot away from its goal-directed task for ethical reasons, but also in the intervening ethical governor which does nothing unless ethical considerations prompt an intervention.

In the initial implementation of the ethical consequence engine [Winfield, Blum, and Liu2014] the underlying engine suggested all possible alternatives to the governor via a discretization process. However the later version [Vanderelst and Winfield2016] presented only a limited number of options for practical reasons – these options were generated by the underlying control system which was therefore clearly implicitly using ethical considerations in order to generate options for the ethical layer. Researchers at Bristol Robotics Lab are now seeking to have the ethical layer request options itself if those generated by the underlying controller are deemed insufficiently ethical444Paul Bremner, personal communication. and this would be the first concrete implementation of ethical proactivity in such systems.

More generally, particularly in cases where the ethical principle of human free will is concerned, it may be necessary for an ethical reasoner to go through an information discovery process in order to determine the wishes of the persons it is interacting with and the strength of those wishes before deciding whether or not it is ethical to intervene. For instance, it is a well established principle that people should be allowed to smoke in their own homes but considerable societal resource is put into making sure they are aware of the risks associated with smoking. We might want a home-support robot to confirm that a home-owner was aware of the dangers of smoking but, if they were, to thereafter allow them to smoke without intervention.

3.5 Scrutability

In general it is desirable for a number of reasons that we should be able to understand how a machine reasons: for instance in order to predict its behaviour, and diagnose errors. However this is particular important where ethical reasoning is concerned. Indeed Moor’s classification of ethical machine agents specifies that explicit agents, such as we consider here, should be able to justify their choices [Moor2006].

While moral philosophy has reached no consensus about whether morality is absolute or relative and societally determined, we view ethical machine reasoning as reasoning about circumstances where a systems actions may impact on the values of a community or individual. Moor refers to these as ethical impact agents. If some concept has attained the status of a moral value, then it has assumed a place of critical importance. Therefore, while people may accept (even if they are irritated) that their SatNav sometimes chooses strange routes without explanation, they are less likely to accept that some system has chosen to violate their privacy and can offer no explanation for why it did so. The ability to understand how a system has reached some decision is variously referred to as explainability or scrutability.

There are a number of forms scrutability [Caminada et al.2014] can take, from being able to inspect design/requirements documents that set out the rules of ethical behaviour the robot is following to the ability to extract an explanation from the robot after a decision that has been made to justify that decision or reconstruct the decision-making process after some problem arises (e.g., an ethical black box [Winfield and Jirotka2017]).

We see this need for scrutability in a number of the systems we survey. Both the ethical governors and GenEth can explicitly justify their reasoning and the use of BDI style agents in Ethan

 also points to this concern, since their reasoning is based upon logic programming, which in turn is based upon logical deduction using explicit rules. While such derivations can be complex to follow they are designed to mimic an account of human reasoning.

3.6 Verifiability

A key value in (arguably) all human societies is human well-being, which manifests ethically as considerations around human safety (among other things). Where a system is deemed to be safety critical we are used to requiring high standards of verification. We would argue that these considerations extend to ethically critical systems. If a value is of sufficient importance to a community to be considered a moral value then we should expect high standards of verification for any system with the potential to have a serious negative impact on that value.

Since, as we have noted, it is beyond our capability to simply insist autonomous systems maximise human well-being we are instead forced to encode ethical reasoning as a set of rules and these sets of rules are likely to be complex – for instance even the fairly simple case study examined in [Anderson, Anderson, and Berenz2016] partitions the space of options into 13 regions, each representing a different combination of the ethical features of a situation. There is therefore scope for error both in implementing a stated set of rules into a machine and expressing the rules so that they do indeed reflect our values. Both [Dennis, Fisher, and Winfield2015] and [Dennis et al.2016a] consider formal properties for machine ethics systems that can be checked by model-checking so long as the system itself has been implemented with such verifiability in mind.

4 Architecture: Multiple Ethical Governors

We propose a generic architecture for ethical reasoning shown in Figure 1. In this architecture an ethical arbiter reasons using evidence provided by a number of evidential reasoners each of which is customised to reason appropriately about some particular ethical feature of the domain. The underlying autonomous system communicates its options to the ethical arbiter, these options are assessed by the evidential reasoners which convert information about the options into a logical or equational form which the ethical arbiter then reasons over. The ethical arbiter then communicates the result of this reasoning back to the autonomous system.

Figure 1: An Architecture for Ethical Machine Reasoning

The arbiter itself should be declarative in nature (i.e. the programming should focus on expressing the logic of the computation as opposed to its control flow). Declarative programming supports scrutability at design time (since the program itself should focus on the outcomes of execution as opposed to how those outcomes are generated) and declarative programming paradigms in general also have better support for verification. Logic programs, BDI agents and constraint reasoners are all examples of declarative programs.

One advantage we claim for this architecture is it allows us to allocate some of the challenges faced by ethical machine reasoning to different parts of the system. Scrutability and multi-objective reasoning are the preserve of the ethical arbiter, while real time concerns can be partitioned into those requiring real time evaluation of the situation (which is the concern of the evidential reasoners) and efficient declarative reasoning (the concern of the arbiter). The modularity also gives us the potential to verify the ethical reasoning itself separately (e.g., following the methodology in [Dennis et al.2016b]) from any verification of the accuracy of the evidential reasoners.

In Figure 1, we have included a “politeness reasoner”. This is because many of the considerations that apply to ethical machine reasoning we believe also apply to machine reasoning about social norms – in particular that such reasoning is multi-objective and needs to be proactive. It is therefore possible to imagine such an architecture being extended to cover more general normative reasoning as well as specifically ethical reasoning.

Many design choices exist within this architecture such as whether the evidential reasoners and/or the arbiter can suggest or request new actions/plans; the nature of the evidence produced (which could potentially contain information pertaining to certainty, severity, who is impacted, how many people are impacted and so on); what constitutes an atomic ethical feature that grounds out reasoning; and so on. The architecture also allows rich or sparse logics to be used by the arbiter.

5 Conclusion

We here address the challenges posed by explicit ethical machine reasoning that are not related specifically to the philosophical uncertainty surrounding the subject matter.

We have argued that explicit ethical machine reasoning faces challenges relating to its multi-objective nature, its frequent requirement for real time process, the heterogenous nature of the evidence it needs to reason about and challenges relating to its scrutability and verifiability. Taken together we believe these challenges make ethical machine reasoning a sub-field of interest not just to philosophers and those interested in formal reasoning about ethics but also to those interested in the implementation of machine reasoning in general.

We have proposed a generic architecture (see also [Dennis and Fisher2017]) which we consider suitable for explicit ethical machine reasoning which, among other things, modularises the ethical reasoning component and so allows some separation of the various challenges into distinct sub-systems, providing routes for tackling these problems. None of the challenges highlighted in this paper are solved by the architecture but it is our intention in future work to implement the architecture and use it as a vehicle for tackling the various practical challenges posed by explicit ethical machine reasoning.

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