Advancing Data and Equity in NFTs and Shaping a Fairer Metaverse

Table of Links

Abstract and 1 Introduction

2 Methodology

3 Results

3.1 CryptoPunks

3.2 Aggregate NFT Market

4 Discussion

5. Conclusion/ Acknowledgements/ References

A Appendix

A.1 Implementation Details

A.2 Detailed NFT Information & A.3 Google NFT Searches Map

5. Conclusion

In this paper, we provide a rigorous statistical analysis of the gender and race biases of CryptoPunks and the NFT market as a whole. We found that gender bias is not statistically significant for CryptoPunks, but racial bias is. Regardless of how we remove outliers, whether we apply a log transform, or whether we used a paired or unpaired t-test, our conclusion remains consistent. As CryptoPunks are well-known and commonly credited for starting the rise of NFTs, biases in prices reflect early-investors’ perceptions of NFTs representing different races.

When we analyzed the NFT market as a whole, we also found there was not a statistically significant difference in price between male and female NFTs. For future work, we plan to label race for more NFT datasets to be able to conclude whether the trend of lighter-skinned CryptoPunks being sold for more than darker-skinned CryptoPunks holds for the general NFT market. We believe this price disparity may be due to the demographics of NFT investors. For avatars that are NFTs, people may tend to buy ones that look similar to their appearance.

Investigating the gender and race biases are important from a culture standpoint due to the popularity of NFTs. As the metaverse is at its seed stage of creation with egalitarian values at the forefront of decentralization, it is important that inequity and biases in society do not propagate into it. Identifying these racial biases is the first step to drive initiatives that bring equity to NFTs and the metaverse. Possible countermeasures of improving fairness include raising awareness to the issue and increasing access to NFTs to other parts of the world, especially in developing countries. We hope future investors will purchase NFTs with racial sensitivity in mind.

6. Acknowledgements

This work is supported by the MIT Advanced Undergraduate SuperUROP program and supported by the National Science Foundation under Grant DGE-1747486. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

References

[1] I Antoniou et al. “On the log-normal distribution of stock market data”. In: Physica A: Statistical Mechanics and its Applications 331.3-4 (2004), pp. 617–638.

[2] Raynor de Best. U.S. NFT user characteristics 2021. May 2022. url: https://www.statista. com/statistics/1265821/us-nft-user-demographics/.

[3] Sheila Bsteh and Filip Vermeylen. “From Painting to Pixel: Understanding NFT Artworks”. In: Retrieved June 15 (2021), p. 2021.

[4] Team Colormatics. NFT audience insights: Who buys nfts and why? url: https://www.colormatics.com/article/nft-audience-insights-whos-buying-nfts-and-why/.

[5] Cryptopunksnotdead. Programming-cryptopunks. May 2021. url: https//github.com /cryptopunksnotdead/programming-cryptopunks/blob/master/07_humans.md.

[6] Dipanjan Das et al. “Understanding security Issues in the NFT Ecosystem”. In: arXiv preprint arXiv:2111.08893 (2021).

[7] Michael Dowling. “Fertile LAND: Pricing non-fungible tokens”. In: Finance Research Letters 44 (2022), p. 102096.

[8] Misyrlena Egkolfopoulou and Akayla Gardner. Even in the Metaverse, Not All Identities Are Created Equal. url: https://www.bloomberg. co /news/features/2021-12-06 / cryptopunk-nft-prices-suggest-a-diversity-problem-in-the-metaverse.

[9] Ethereum Whitepaper. 2014. url: https://ethereum.org/en/whitepaper.

[10] Allan Fowler and Johanna Pirker. “Tokenfication-The potential of non-fungible tokens (NFT) for game development”. In: Extended Abstracts of the 2021 Annual Symposium on ComputerHuman Interaction in Play. 2021, pp. 152–157.

[11] Kaggle Dataset: Ethereum NFTs. url: https://www.kaggle.com/datasets/simiotic/ethereum-nfts.

[12] De-Rong Kong and Tse-Chun Lin. “Alternative investments in the Fintech era: The risk and return of Non-Fungible Token (NFT)”. In: Available at SSRN 3914085 (2021).

[13] Mieszko Mazur. “Non-Fungible Tokens (NFT). The Analysis of Risk and Return”. In: Available at SSRN 3953535 (2021).

[14] Matthieu Nadini et al. “Mapping the NFT revolution: market trends, trade networks, and visual features”. In: Scientific reports 11.1 (2021), pp. 1–11.

[15] OpenSea API Docs: Retrieve Collections. url: https://docs.opensea.io/reference/retrieving-collections.

[16] Andrei-Dragos Popescu. “Non-Fungible Tokens (NFT)–Innovation beyond the craze”. In: 5th International Conference on Innovation in Business, Economics and Marketing Research. 2021.

[17] PySpark. url: https://spark.apache.org/docs/latest/api/python.

[18] Luisa Schaar and Stylianos Kampakis. “Non-fungible tokens as an alternative investment: Evidence from cryptopunks”. In: The Journal of The British Blockchain Association (2022), p. 31949.

[19] John W Tukey and Donald H McLaughlin. “Less vulnerable confidence and significance procedures for location based on a single sample: Trimming/Winsorization 1”. In: Sankhy¯a: The Indian Journal of Statistics, Series A (1963), pp. 331–352.

[20] Sandra Upson. The 10,000 faces that launched an NFT revolution. Nov. 2021. url: https://www.wired.com/story/the-10000-faces-that-launched-an-nft-revolution/.

[21] Pauli Virtanen et al. “SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python”. In: Nature Methods 17 (2020), pp. 261–272. doi: 10.1038/s41592-019-0686-2.

[22] Ronald E Walpole et al. Probability and Statistics for Engineers and Scientists. 9th ed. Prentice Hall, 2012.