18 research outputs found
In conversation with Artificial Intelligence: aligning language models with human values
Large-scale language technologies are increasingly used in various forms of
communication with humans across different contexts. One particular use case
for these technologies is conversational agents, which output natural language
text in response to prompts and queries. This mode of engagement raises a
number of social and ethical questions. For example, what does it mean to align
conversational agents with human norms or values? Which norms or values should
they be aligned with? And how can this be accomplished? In this paper, we
propose a number of steps that help answer these questions. We start by
developing a philosophical analysis of the building blocks of linguistic
communication between conversational agents and human interlocutors. We then
use this analysis to identify and formulate ideal norms of conversation that
can govern successful linguistic communication between humans and
conversational agents. Furthermore, we explore how these norms can be used to
align conversational agents with human values across a range of different
discursive domains. We conclude by discussing the practical implications of our
proposal for the design of conversational agents that are aligned with these
norms and values
Permissible secrets
This article offers an account of the information condition on morally valid consent in the context of sexual relations. The account is grounded in rights. It holds that a person has a sufficient amount of information to give morally valid consent if, and only if, she has all the information to which she has a claim-right. A person has a claim-right to a piece of information if, and only if, a. it concerns a deal-breaker for her; b. it does not concern something that her partner has a strong interest in protecting from scrutiny, sufficient to generate a privilege-right; c.i. her partner is aware of the information to which her deal-breaker applies; or c.ii. her partner ought to be held responsible for the fact that he is not aware of the information to which her deal-breaker applies; and finally, d. she has not waived or forfeited her claim-right. Although we present this account in the context of sexual relations, we believe a virtue of the account is that it can be easily translated into other contexts
Manifestations of Xenophobia in AI Systems
Xenophobia is one of the key drivers of marginalisation, discrimination, and
conflict, yet many prominent machine learning (ML) fairness frameworks fail to
comprehensively measure or mitigate the resulting xenophobic harms. Here we aim
to bridge this conceptual gap and help facilitate safe and ethical design of
artificial intelligence (AI) solutions. We ground our analysis of the impact of
xenophobia by first identifying distinct types of xenophobic harms, and then
applying this framework across a number of prominent AI application domains,
reviewing the potential interplay between AI and xenophobia on social media and
recommendation systems, healthcare, immigration, employment, as well as biases
in large pre-trained models. These help inform our recommendations towards an
inclusive, xenophilic design of future AI systems
Power to the People? Opportunities and Challenges for Participatory AI
Participatory approaches to artificial intelligence (AI) and machine learning
(ML) are gaining momentum: the increased attention comes partly with the view
that participation opens the gateway to an inclusive, equitable, robust,
responsible and trustworthy AI.Among other benefits, participatory approaches
are essential to understanding and adequately representing the needs, desires
and perspectives of historically marginalized communities. However, there
currently exists lack of clarity on what meaningful participation entails and
what it is expected to do. In this paper we first review participatory
approaches as situated in historical contexts as well as participatory methods
and practices within the AI and ML pipeline. We then introduce three case
studies in participatory AI.Participation holds the potential for beneficial,
emancipatory and empowering technology design, development and deployment while
also being at risk for concerns such as cooptation and conflation with other
activities. We lay out these limitations and concerns and argue that as
participatory AI/ML becomes in vogue, a contextual and nuanced understanding of
the term as well as consideration of who the primary beneficiaries of
participatory activities ought to be constitute crucial factors to realizing
the benefits and opportunities that participation brings.Comment: To appear in the proceeding of EAAMO 202
A wireless electro-optic platform for multimodal electrophysiology and optogenetics in freely moving rodents
This paper presents the design and the utilization of a wireless electro-optic platform to perform simultaneous multimodal electrophysiological recordings and optogenetic stimulation in freely moving rodents. The developed system can capture neural action potentials (AP), local field potentials (LFP) and electromyography (EMG) signals with up to 32 channels in parallel while providing four optical stimulation channels. The platform is using commercial off-the-shelf components (COTS) and a low-power digital field-programmable gate array (FPGA), to perform digital signal processing to digitally separate in real time the AP, LFP and EMG while performing signal detection and compression for mitigating wireless bandwidth and power consumption limitations. The different signal modalities collected on the 32 channels are time-multiplexed into a single data stream to decrease power consumption and optimize resource utilization. The data reduction strategy is based on signal processing and real-time data compression. Digital filtering, signal detection, and wavelet data compression are used inside the platform to separate the different electrophysiological signal modalities, namely the local field potentials (1–500 Hz), EMG (30–500 Hz), and the action potentials (300–5,000 Hz) and perform data reduction before transmitting the data. The platform achieves a measured data reduction ratio of 7.77 (for a firing rate of 50 AP/second) and weights 4.7 g with a 100-mAh battery, an on/off switch and a protective plastic enclosure. To validate the performance of the platform, we measured distinct electrophysiology signals and performed optogenetics stimulation in vivo in freely moving rondents. We recorded AP and LFP signals with the platform using a 16-microelectrode array implanted in the primary motor cortex of a Long Evans rat, both in anesthetized and freely moving conditions. EMG responses to optogenetic Channelrhodopsin-2 induced activation of motor cortex via optical fiber were also recorded in freely moving rodents
Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models
Large language models produce human-like text that drive a growing number of
applications. However, recent literature and, increasingly, real world
observations, have demonstrated that these models can generate language that is
toxic, biased, untruthful or otherwise harmful. Though work to evaluate
language model harms is under way, translating foresight about which harms may
arise into rigorous benchmarks is not straightforward. To facilitate this
translation, we outline six ways of characterizing harmful text which merit
explicit consideration when designing new benchmarks. We then use these
characteristics as a lens to identify trends and gaps in existing benchmarks.
Finally, we apply them in a case study of the Perspective API, a toxicity
classifier that is widely used in harm benchmarks. Our characteristics provide
one piece of the bridge that translates between foresight and effective
evaluation.Comment: Accepted to NeurIPS 2022 Datasets and Benchmarks Track; 10 pages plus
appendi
Model evaluation for extreme risks
Current approaches to building general-purpose AI systems tend to produce
systems with both beneficial and harmful capabilities. Further progress in AI
development could lead to capabilities that pose extreme risks, such as
offensive cyber capabilities or strong manipulation skills. We explain why
model evaluation is critical for addressing extreme risks. Developers must be
able to identify dangerous capabilities (through "dangerous capability
evaluations") and the propensity of models to apply their capabilities for harm
(through "alignment evaluations"). These evaluations will become critical for
keeping policymakers and other stakeholders informed, and for making
responsible decisions about model training, deployment, and security