Business of Science and Technology Initiative

Permanent URI for this collection

https://hdl.handle.net/1813/11654

The Business of Science and Technology Initiative (BSTI) was established in 2006 between the College of Engineering and the Business School at Cornell University to create a new platform and business growth model for connecting the world-wide corporate community with major research universities. BSTI's objective is to "Innovate the Innovation Process" by facilitating the development and deployment of new technologies in application rich markets.

Unlike traditional "sponsored research" projects, all BSTI efforts are tailored to real-world innovation with corporate involvement in today's open innovation environment. The BSTI accomplishes both innovation and educational goals by forming innovation teams composed of engineering and business students, as well as professors and PhD students. Each team is lead by a BSTI innovation manager with experience in both technical and business innovation. Working in a pre-seed company mode, the interdisciplinary team iterates between uncertain markets and uncertain technology, delivering information and/or prototypes of interest to the corporations. A typical corporate project develops strategic outlooks in areas that are beyond the immediate investment horizon for a corporation but critical to the corporation's innovation and growth plans. A BSTI project iterates between engineering inventions, embodiments, manufacturing evaluations and market opportunity assessments.

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Now showing 1 - 5 of 5

Optimizing Global Expansion in Media and Entertainment Through AI-Driven Engagement and Cultural Relevance
Shaikh, Rumiza Shakeel (Cornell University, 2025-06-01)
This paper explores how media and entertainment (M&E) companies can achieve scalable global expansion while preserving cultural authenticity and user trust. As emerging markets become key drivers of digital growth, companies face the dual challenge of localizing experiences without diluting brand identity. The paper examines how artificial intelligence (AI) is being leveraged to personalize content, curate user journeys, and adapt digital interfaces across geographies. Through case studies of platforms like Netflix, Spotify, and Disney+ Hotstar, it analyzes the strategic role of hybrid AI-human workflows, regional content hubs, and modular branding systems. Special attention is given to the ethical implications of algorithmic decision-making and the importance of transparency in AI-driven engagement. Ultimately, the paper provides a strategic framework for global M&E leaders to balance innovation, localization, and trust while navigating the complexities of digital transformation in emerging economies.
LocaliSense: Architecting Culturally Adaptive AI for Global Information Access
Shaikh, Rumiza Shakeel (Cornell University, 2025-05-25)
As generative AI becomes a central interface for knowledge and communication, its outputs often default to Western linguistic norms and cultural metaphors. This reduces their relevance, usability, and trustworthiness for users in emerging markets and culturally diverse regions. LocaliSense addresses this limitation by serving as a post-processing localization layer that adapts AI-generated content across any data domain to reflect regional tone, semantic simplicity, contextual relevance, and culturally appropriate analogies. This white paper introduces the design, architecture, and applications of LocaliSense across fields such as education, healthcare, finance, civic technology, and media. By integrating location metadata, analogy transformation, and multilingual simplification engines, LocaliSense enhances both user trust and comprehension in varied linguistic and cultural settings. The system offers a scalable framework for building culturally fluent AI experiences and supports the broader goal of making AI outputs universally accessible and meaningful. We explore product implications for platforms including search, generative models, enterprise tools, and civic assistants, positioning LocaliSense as a foundational infrastructure layer for globally adaptive AI design.
Improving Inclusion in AI-Based Candidate Disparate Impact and Counterfactual Testing
Kotharu, Aishwarya; Shaikh, Rumiza Shakeel (Cornell University, 2025-04-26)
Automated recruitment platforms, such as Workday and iCIMS, increasingly rely on machine learning (ML) models to streamline candidate selection. However, these systems may inadvertently reinforce existing biases in hiring processes. This paper proposes a quantitative and empirical approach to evaluating and improving fairness in AI-based hiring pipelines. We focus on Disparate Impact (DI) measurement and Counterfactual Fairness testing to audit a representative ATS—Workday’s AI screening engine. We compute DI across various demographic groups, demonstrating how current candidate scoring mechanisms fail the "four-fifths" threshold (DI < 0.8) in simulated recruitment scenarios. To mitigate such inequities, we introduce a bias correction model that rebalances feature weights in post-processing. Among several variables, we identify the "education prestige score" as a key contributor to disparate treatment. Experimental results on semi-synthetic datasets show that reweighting or masking this feature significantly reduces DI disparity while preserving predictive performance (AUC drop <1%). This work provides a replicable methodology for fairness auditing in enterprise-grade ATS, offering a pathway to more equitable recruitment practices.
The Rise of AI Agents in Hiring: Trends, Tensions, and Future Trajectories
Shaikh, Rumiza Shakeel; Kotharu, Aishwarya (Cornell University, 2025-04-25)
This whitepaper explores the increasing integration of artificial intelligence (AI) agents into recruitment and hiring systems worldwide. AI agents—automated software systems that perform tasks such as candidate screening, interviewing, assessment, and recommendation—are becoming central tools in modern talent acquisition. Their adoption is driven by the need for efficiency, scalability, and data-informed decision-making in a labor market that is both globalized and digitally enabled. While these technologies offer clear benefits to employers, including faster hiring processes and reduced administrative burden, they also raise important challenges. Concerns around algorithmic bias, data privacy, transparency, and regulatory compliance are growing as these tools become more sophisticated and widely implemented. This paper provides a balanced and neutral analysis of both the opportunities and risks associated with the use of AI in hiring. The analysis draws from real-world case studies, current industry practices, and international policy developments, including emerging legislation such as the European Union’s AI Act, U.S. anti-discrimination frameworks, and data governance models in Asia. It also categorizes AI agents by function—such as conversational bots, resume filters, assessment tools, and predictive analytics platforms—and assesses their operational impact. Designed to inform HR professionals, policymakers, researchers, and AI developers, this whitepaper highlights the strategic considerations necessary for the responsible use of AI in recruitment. It argues that the effectiveness of AI agents depends not only on technical performance, but also on ethical implementation, stakeholder trust, and regulatory alignment. The findings contribute to a broader understanding of how AI technologies are shaping the future of work and workforce management.
Market-Driven Innovation
Wankerl, Andreas; DiCicco, Guy F.; Burch, Andrew T.; Charpentier, Erik L.; Chen, Aohan; Goel, Vaibhav; Levy, Heather D.; Perri, Melissa J.; Strandberg, Joseph L.; Vargas Vila, Nicholas; Worthington, Casey D.; Lee, Mia Junghae; Sadashivan, Sharath; Fitzgerald, Eugene A.; Kuberka, Cheryl J.; Olson, Donald E. (2008-12-18T22:34:46Z)
A new method for starting the iterative innovation process from the market side based on a sociological trend has been developed. It eliminates the traditional difference between the innovators and the sociological group that carries this trend, which can only be achieved by combining real-world innovation with innovation education. The method for market need discovery is presented as a step-by-step process with detailed reasoning, followed by a real-world example that details the outcomes at every step along the way. The example concludes with a detailed description of the outcome after the first innovation iteration cycle. The richness of the resulting concept demonstrates that an innovation process can be successfully started from the market side via the proposed method.

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