Pluralistic alignment is concerned with ensuring that an AI system's objectives and behaviors are in harmony with the diversity of human values and perspectives. In this paper we study the notion of pluralistic alignment in the context of agentic AI, and in particular in the context of an agent that is trying to learn a policy in a manner that is mindful of the values and perspective of others in the environment. To this end, we show how being considerate of the future wellbeing and agency of other (human) agents can promote a form of pluralistic alignment.

A few short years ago, personal digital assistants like Amazon's Alexa, Apple's Siri, and Google Assistant sounded futuristic. Now, the future is here and this future is embedded, augmented, and ubiquitous. Digital assistants can be found in your office, home, car, hotel, phone, and many other places. They have recently undergone a massive transformation and run on operating systems that are fueled by artificial intelligence (A.I.). They observe and collect data in real-time and have the capability to pull information from different sources such as smart devices and cloud services and put the information into context using A.I. to make sense of the situation.

A few short years ago, personal digital assistants like Amazon's Alexa, Apple's Siri and Google Assistant sounded futuristic. Now, the future is here and this future is embedded, augmented and ubiquitous. Digital assistants can be found in your office, home, car, hotel, phone and many other places. They have recently undergone massive transformation and run on operating systems that are fuelled by artificial intelligence (AI). They observe and collect data in real-time and have the capability to pull information from different sources such as smart devices and cloud services and put the information into context using AI to make sense of the situation.

Since March, when an autonomous vehicle killed a pedestrian in Arizona, forecasts for AVs have been decidedly less optimistic. But autonomous vehicle promoters are undeterred. AI entrepreneur Andrew Ng contends that self-driving cars will be safe for pedestrians when walkers and cyclists conform to their limitations. "What we tell people is, 'Please be lawful and please be considerate,'" he told Bloomberg. Peter Norton is an associate professor in the Department of Engineering and Society at the University of Virginia. He is the author of Fighting Traffic: The Dawn of the Motor Age in the American City.

Abstract-- Driving is a social activity: drivers often indicate their intent to change lanes via motion cues. We consider mixed-autonomy traffic where a Human-driven V ehicle (HV) and an Autonomous V ehicle (A V) drive together . We propose a planning framework where the degree to which the A V considers the other agent's reward is controlled by a selfishness factor . We test our approach on a simulated two-lane highway where the A V and HV merge into each other's lanes. In a user study with 21 subjects and 6 different selfishness factors, we found that our planning approach was sound and that both agents had less merging times when a factor that balances the rewards for the two agents was chosen. Our results on double lane merging suggest it to be a nonzero-sum game and encourage further investigation on collaborative decision making algorithms for mixed-autonomy traffic. Driving is a social activity: drivers indicate their willingness to change lanes by subtle cues such as eye contact, or by not-so-subtle cues such as adjusting their speed and position [1]. There has been impressive demonstrations of Autonomous V ehicle (A V) technology [2]-[4], however one of the remaining challenges in this area is reading those cues to estimate the intentions of other agents as well as using cues to communicate the intentions of the A V . As A Vs become commonplace, the situations where A V's and Human-driven V ehicles (HV) interact will increase.

Nothing will make you hate humans--capricious, volatile, unplanned, erratic humans--like sitting in the back of self-driving car. When I hitched a ride in one, a white and orange General Motors Cruise autonomous vehicle during a press event in San Francisco on Tuesday, every movement was a cause for alarm. Two walkers darted out in front of the car during my roughly 20-minute, 3-mile ride, blissfully ignorant that they were trusting their lives to a piece of software. Two cyclists made unexpected but sweeping turns. Human-operated vehicles whipped around corners and rolled through stop signs.

We study the existence and computational complexity of coalitional stability concepts based on social networks. Our concepts represent a natural and rich combinatorial generalization of a recent notion termed partition equilibrium. We assume that players in a strategic game are embedded in a social (or, communication) network, and there are coordination constraints defining the set of coalitions that can jointly deviate in the game. A main feature of our approach is that players act in a "considerate" fashion to ignore potentially profitable (group) deviations if the change in their strategy may cause a decrease of utility to their neighbors in the network. We explore the properties of such considerate equilibria in application to the celebrated class of resource selection games (RSGs). Our main result proves existence of a super-strong considerate equilibrium in all symmetric RSGs with strictly increasing delays, for any social network among the players and feasible coalitions represented by the set of cliques. The existence proof is constructive and yields an efficient algorithm. In fact, the computed considerate equilibrium is a Nash equilibrium for a standard RSG, thus showing that there exists a state that is stable against selfish and considerate behavior simultaneously. Furthermore, we provide results on convergence of considerate dynamics.