At Target, creating relevant guest experiences at scale takes more than great creative — it takes great data. In this session, we’ll explore how Target’s Data Science team is using first-party data, machine learning, and GenAI to personalize marketing across every touchpoint.

You’ll hear how we’re building intelligence into the content supply chain, turning unified customer signals into actionable insights, and using AI to optimize creative, timing, and messaging — all while navigating a privacy-first landscape. Whether it’s smarter segmentation or real-time decisioning, we’re designing for both scale and speed.

As organizations and individual workers increasingly adopt generative AI (GenAI) to improve productivity, there is limited understanding of how different modes of human-AI interactions affect worker experience.

In this study, we examine the ordering effect of human-AI collaboration on worker experience through a series of pre-registered laboratory and online experiments involving common professional writing tasks. We study three collaboration orders: AI-first when humans prompt AI to draft the work and then improve it, human-first when humans draft the work and ask AI to improve it, and no-AI. Our results reveal an important trade-off between worker productivity and worker experience: while workers completed the writing draft more quickly in the AI-first condition than in the human-first condition, they reported significantly lower autonomy and efficacy. This negative ordering effect affected primarily female workers, not male workers.

Furthermore, being randomly assigned to a collaboration mode increased workers’ likelihood of choosing the same mode for similar tasks in the future, especially for the human-first collaboration mode. In addition, writing products generated with the use of GenAI were longer, more complex, and required higher grade levels to comprehend. Together, our findings highlight the potential hidden risks of integrating GenAI into workflow and the imperative of designing human-AI collaborations to balance work productivity with human experiences.

Artificial Intelligence (AI) and Generative AI (GenAI) are marketed as upgrades to data-driven decision making in education, promising faster predictions, personalization, and adaptive interventions. Yet these systems do not address the fundamental problems like over-reliance on quantifiable metrics, bias, inequity, and lack of transparency, embedded in educational data practices; they amplify them.

Across platforms such as Learning Management Systems (LMS), institutional dashboards, and predictive models, what is counted as “data” remains narrow: logins, clicks, scores, demographics, and test results. Excluded are lived experiences, complex identities, and structural inequities. These omissions are not accidental; they are design choices shaped by institutional priorities and power.

Drawing on O’Neil and Broussard, this session highlights how data-driven systems risk misinterpretation, reductionism, and exclusion. Participants will engage with scenarios that demonstrate both the promises and pitfalls of triangulating educational data. Together, we will discuss how such data might be misinterpreted, reduced, or stripped of context when filtered through AI systems.

As a starting point to navigate these problems, Critical Data Literacy is introduced as a framework for reimagining data practices through comprehension, critique, and participation. It equips participants engaging with data-driven systems in education and beyond to interrogate how data is produced, whose knowledge counts, and what is excluded or marginalized.

Participants will leave with reflective questions to guide their own practice: Better for whom? What is not on the screen? Whose goals are being personalized? Without this lens, AI risks accelerating inequities under the guise of objectivity.

Large language models are powerful, but their true potential emerges when they evolve into AI agents which are systems that can reason, plan, and take action autonomously. My talk will explore the shift from using models as passive tools to designing agents that actively interact with data, systems, and people.

I will cover: - Gen AI and Agentic AI – How are These Different - Single Agent (monolithic) and Multi Agent Architectures (modular / distributed) - Open Source and Closed Source AI Systems - Challenges of Integrating Agents with Existing Systems

I will break down the technical building blocks of AI agents, including memory, planning loops, tool integration, and feedback mechanisms. Examples will be used to highlight how agents are being used in workflow automation, knowledge management, and decision support.

I will wrap up with where limitations of AI Agents still pose risks: - Assessing Maturity Cycle of Agents - Cybersecurity Risks of Agents

By the end, attendees will understand: - What makes AI agents different from LLMs - Technical considerations required to build AI Agents responsibly - Applicable knowledge to begin experimenting with agents.