252 research outputs found
Society-in-the-Loop: Programming the Algorithmic Social Contract
Recent rapid advances in Artificial Intelligence (AI) and Machine Learning
have raised many questions about the regulatory and governance mechanisms for
autonomous machines. Many commentators, scholars, and policy-makers now call
for ensuring that algorithms governing our lives are transparent, fair, and
accountable. Here, I propose a conceptual framework for the regulation of AI
and algorithmic systems. I argue that we need tools to program, debug and
maintain an algorithmic social contract, a pact between various human
stakeholders, mediated by machines. To achieve this, we can adapt the concept
of human-in-the-loop (HITL) from the fields of modeling and simulation, and
interactive machine learning. In particular, I propose an agenda I call
society-in-the-loop (SITL), which combines the HITL control paradigm with
mechanisms for negotiating the values of various stakeholders affected by AI
systems, and monitoring compliance with the agreement. In short, `SITL = HITL +
Social Contract.'Comment: (in press), Ethics of Information Technology, 201
Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
NLP tasks are often limited by scarcity of manually annotated data. In social
media sentiment analysis and related tasks, researchers have therefore used
binarized emoticons and specific hashtags as forms of distant supervision. Our
paper shows that by extending the distant supervision to a more diverse set of
noisy labels, the models can learn richer representations. Through emoji
prediction on a dataset of 1246 million tweets containing one of 64 common
emojis we obtain state-of-the-art performance on 8 benchmark datasets within
sentiment, emotion and sarcasm detection using a single pretrained model. Our
analyses confirm that the diversity of our emotional labels yield a performance
improvement over previous distant supervision approaches.Comment: Accepted at EMNLP 2017. Please include EMNLP in any citations. Minor
changes from the EMNLP camera-ready version. 9 pages + references and
supplementary materia
Pareto Optimality and Strategy Proofness in Group Argument Evaluation (Extended Version)
An inconsistent knowledge base can be abstracted as a set of arguments and a
defeat relation among them. There can be more than one consistent way to
evaluate such an argumentation graph. Collective argument evaluation is the
problem of aggregating the opinions of multiple agents on how a given set of
arguments should be evaluated. It is crucial not only to ensure that the
outcome is logically consistent, but also satisfies measures of social
optimality and immunity to strategic manipulation. This is because agents have
their individual preferences about what the outcome ought to be. In the current
paper, we analyze three previously introduced argument-based aggregation
operators with respect to Pareto optimality and strategy proofness under
different general classes of agent preferences. We highlight fundamental
trade-offs between strategic manipulability and social optimality on one hand,
and classical logical criteria on the other. Our results motivate further
investigation into the relationship between social choice and argumentation
theory. The results are also relevant for choosing an appropriate aggregation
operator given the criteria that are considered more important, as well as the
nature of agents' preferences
Inducing Peer Pressure to Promote Cooperation
Cooperation in a large society of self-interested individuals is notoriously difficult to achieve when the externality of one individual's action is spread thin and wide on the whole society. This leads to the ‘tragedy of the commons’ in which rational action will ultimately make everyone worse-off. Traditional policies to promote cooperation involve Pigouvian taxation or subsidies that make individuals internalize the externality they incur. We introduce a new approach to achieving global cooperation by localizing externalities to one's peers in a social network, thus leveraging the power of peer-pressure to regulate behavior. The mechanism relies on a joint model of externalities and peer-pressure. Surprisingly, this mechanism can require a lower budget to operate than the Pigouvian mechanism, even when accounting for the social cost of peer pressure. Even when the available budget is very low, the social mechanisms achieve greater improvement in the outcome.Martin Family Fellowship for SustainabilityU.S. Army Research Laboratory (Cooperative Agreement W911NF-09-2-0053)United States. Air Force Office of Scientific Research (Award FA9550-10-1-0122
Small cities face greater impact from automation
The city has proven to be the most successful form of human agglomeration and
provides wide employment opportunities for its dwellers. As advances in
robotics and artificial intelligence revive concerns about the impact of
automation on jobs, a question looms: How will automation affect employment in
cities? Here, we provide a comparative picture of the impact of automation
across U.S. urban areas. Small cities will undertake greater adjustments, such
as worker displacement and job content substitutions. We demonstrate that large
cities exhibit increased occupational and skill specialization due to increased
abundance of managerial and technical professions. These occupations are not
easily automatable, and, thus, reduce the potential impact of automation in
large cities. Our results pass several robustness checks including potential
errors in the estimation of occupational automation and sub-sampling of
occupations. Our study provides the first empirical law connecting two societal
forces: urban agglomeration and automation's impact on employment
Learning in Repeated Games: Human Versus Machine
While Artificial Intelligence has successfully outperformed humans in complex
combinatorial games (such as chess and checkers), humans have retained their
supremacy in social interactions that require intuition and adaptation, such as
cooperation and coordination games. Despite significant advances in learning
algorithms, most algorithms adapt at times scales which are not relevant for
interactions with humans, and therefore the advances in AI on this front have
remained of a more theoretical nature. This has also hindered the experimental
evaluation of how these algorithms perform against humans, as the length of
experiments needed to evaluate them is beyond what humans are reasonably
expected to endure (max 100 repetitions). This scenario is rapidly changing, as
recent algorithms are able to converge to their functional regimes in shorter
time-scales. Additionally, this shift opens up possibilities for experimental
investigation: where do humans stand compared with these new algorithms? We
evaluate humans experimentally against a representative element of these
fast-converging algorithms. Our results indicate that the performance of at
least one of these algorithms is comparable to, and even exceeds, the
performance of people
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