The “Assembly Line” for the Information Age

Human-Computer Cooperation for Large-Scale Product Classification

Jianfu ChenComputer Science Department,

Stony Brook University

Machines Transform Human History

People have always been seeking the optimal way of integrating

machine and human labor.

20th Century

Ford Assembly LineIntegrates Machine and Human Labor Efficiently

21st Century – Information Age

“Mass Production” of Information

We want to find the optimal ways to integrate machine and human intelligence.

• NOT all products could be produced fully automatically by machines– assembly line integrated machine & human labor

• NOT all information can be produced fully automatically by computers– We want to find optimal ways to integrate

machine and human intelligence• What’s the “Assembly Line” for the

Information Age?

A Case Study: Large scale product classification

Kindle Fire HD 8.9" 4G LTE Wireless8.9" HD Display, Dolby Audio, Dual-Band Dual-Antenna Wi-Fi, 4G LTE, 32GB or 64GB

Goal: • optimally integrate computer and human effort• Achieve a lower unit cost for product classification• More precisely, optimize the accuracy-cost tradeoff

An “Assembly Line” for Human Computer Cooperation

3Com V.35 cable V.35 cable ( DTE ) - DB-50 (M) - M/34 (V.35) (M) - 10 ft

26121609

A list of K candidate classes

System Accuracy

Machine Accuracy

Human AccuracyX

Cost is Human labor cost, i.e., the salary paid to workers, which is proportional to the working time spent.

=

A quick glance at Accuracy-Cost Relation

• Assume K determines the Accuracy and Cost.• System Accuracy • Machine Accuracy increases as K increases

• Human Accuracy decreases as K increases

• Cost increases as K increases

A quick glance at Accuracy-Cost Relation

There is an optimal cost that gives the highest accuracy.

Towards a more realistic analysis of accuracy-cost relationship

• With the above “assembly line” model, human accuracy and working time are influenced by a set of factors– K– Task difficulty– Expertise

• I am familiar with office supplies, but not familiar with nuts and bolts.

– Cognitive characteristics• Careful, smart, quick• Independent of the task

Use a probabilistic graphical model to capture the cognitive process of human

classification• A probabilistic graphical model shows how the

above different factors interact with each other, and influence the accuracy and cost.

• Specifically, we use Bayesian Network, which characterizes the causal relationships of different factors.

Use a Bayesian Network to predict accuracy and cost

𝑨𝒉 𝑨𝒎

𝑨

𝑻𝒊𝒎𝒆

𝑪𝒐𝒔𝒕

𝑪𝒐𝒈𝑴𝑲

𝑻𝒄 𝑬𝑫

Not only visually intuitive, but also formal

𝑨𝒉 𝑨𝒎

𝑨

𝑻𝒊𝒎𝒆

𝑪𝒐𝒔𝒕

𝑪𝒐𝒈𝑴𝑲

𝑻𝒄 𝑬𝑫

𝑃 (𝐴h=1|𝐷 ,𝐾 ,𝑀 ,𝐶𝑜𝑔 )= 1

1+exp (− (𝑤1𝑇𝐷+𝑤2𝐾 +𝑤3𝑀+𝑤4𝐶𝑜𝑔)−𝛼)

𝑇𝑖𝑚𝑒=𝑤1𝑇𝐷+𝑤2𝐾 +𝑤3𝑀+𝑤4𝐶𝑜𝑔+𝛽

Inference and learning

• with this Bayesian Network, we predict the accuracy and cost by

• Training data– A set of examples with known class labels– We let each human worker work on multiple examples,

record the correctness and the working time• EM algorithm learns the parameters and the

hidden variables

usage of the model

• Predict the accuracy-cost tradeoff– Given certain budget, what’s the highest accuracy

we can achieve?– To achieve certain accuracy, what’s the lowest

expected cost?• How to charge customers?

• Optimally assign the workers to the tasks

Related Works

• time and motion study– Scientific management (Taylorism)

• Crowdsourcing– Amazon Mechanical Turk– learning worker expertise and accuracy

• Item Response Theory– Psychometrics• IQ test, GRE, GMAT

Conclusion

• In information age, we need a new “assembly line” to integrate human and machine intelligence.

• We try to model human accuracy and working time by considering the interactions of a set of relevant factors, using a probabilistic graphical model.

• We use the model to predict the accuracy-cost tradeoff, decide how to charge customers, and optimally assign tasks to human workers.

Thank you!