Building Up a Real Sector Business Confidence Index for Turkey

The two major aims of this study are, firstly, to receive valuable insight into the Business Tendency Survey of the Central Bank of the Republic of Turkey and, secondly, to construct a real sector confidence index by using the questions of the Business Tendency Survey. The most important motivation behind constructing a real sector confidence index is to provide an indicator of short-term business conditions for economic policy makers and business managers by examining business managers' views on general business conditions and their future anticipations. The real sector confidence index is constructed in accordance with statistical criteria and economic theory. Afterwards, the performance of the index in tracking the cyclical features of industrial production index is tested.Business Tendency Survey, Industrial Production Index, Cross-correlation, Principal Component

Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure

As machine learning systems move from computer-science laboratories into the open world, their accountability becomes a high priority problem. Accountability requires deep understanding of system behavior and its failures. Current evaluation methods such as single-score error metrics and confusion matrices provide aggregate views of system performance that hide important shortcomings. Understanding details about failures is important for identifying pathways for refinement, communicating the reliability of systems in different settings, and for specifying appropriate human oversight and engagement. Characterization of failures and shortcomings is particularly complex for systems composed of multiple machine learned components. For such systems, existing evaluation methods have limited expressiveness in describing and explaining the relationship among input content, the internal states of system components, and final output quality. We present Pandora, a set of hybrid human-machine methods and tools for describing and explaining system failures. Pandora leverages both human and system-generated observations to summarize conditions of system malfunction with respect to the input content and system architecture. We share results of a case study with a machine learning pipeline for image captioning that show how detailed performance views can be beneficial for analysis and debugging

Pair implementation

This thesis analyzes the implementation problem under complete information in a societal environment where all agents are grouped in pairs. We introduce a new equilibrium notion called pair-Nash equilibrium and define a related pair-monotonicity notion on social choice rules and show that it is necessary for implementation in pair-Nash equilibrium, i.e., pair-implementation. Moreover, we introduce the pairimplementability condition and prove that it suffices for pair-implementation. Our results extend implementation results of Maskin (1999) and Moore and Repullo (1990) to the pair-structur

Evaluating the Representational Hub of Language and Vision Models

The multimodal models used in the emerging field at the intersection of computational linguistics and computer vision implement the bottom-up processing of the `Hub and Spoke' architecture proposed in cognitive science to represent how the brain processes and combines multi-sensory inputs. In particular, the Hub is implemented as a neural network encoder. We investigate the effect on this encoder of various vision-and-language tasks proposed in the literature: visual question answering, visual reference resolution, and visually grounded dialogue. To measure the quality of the representations learned by the encoder, we use two kinds of analyses. First, we evaluate the encoder pre-trained on the different vision-and-language tasks on an existing diagnostic task designed to assess multimodal semantic understanding. Second, we carry out a battery of analyses aimed at studying how the encoder merges and exploits the two modalities.Comment: Accepted to IWCS 201

Stochastic Privacy

Online services such as web search and e-commerce applications typically rely on the collection of data about users, including details of their activities on the web. Such personal data is used to enhance the quality of service via personalization of content and to maximize revenues via better targeting of advertisements and deeper engagement of users on sites. To date, service providers have largely followed the approach of either requiring or requesting consent for opting-in to share their data. Users may be willing to share private information in return for better quality of service or for incentives, or in return for assurances about the nature and extend of the logging of data. We introduce \emph{stochastic privacy}, a new approach to privacy centering on a simple concept: A guarantee is provided to users about the upper-bound on the probability that their personal data will be used. Such a probability, which we refer to as \emph{privacy risk}, can be assessed by users as a preference or communicated as a policy by a service provider. Service providers can work to personalize and to optimize revenues in accordance with preferences about privacy risk. We present procedures, proofs, and an overall system for maximizing the quality of services, while respecting bounds on allowable or communicated privacy risk. We demonstrate the methodology with a case study and evaluation of the procedures applied to web search personalization. We show how we can achieve near-optimal utility of accessing information with provable guarantees on the probability of sharing data